Search

Your search keyword '"Marie Chavent"' showing total 75 results
Start Over Author "Marie Chavent"
75 results on '"Marie Chavent"'

1. Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits

Author

Marie-Pierre Ellies-Oury, Jean-François Hocquette, Sghaier Chriki, Alexandre Conanec, Linda Farmer, Marie Chavent, and Jérôme Saracco

Subjects
optimization, meat quality, trade-off, meat standards Australia, carcass, bovine, Chemical technology, TP1-1185
Abstract

The beef industry is organized around different stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these differing perspectives. Then, various optimization models that might integrate all these expectations are described. The final goal is to define practices that could increase value for animal production, carcasses and meat whilst simultaneously meeting the main expectations of the beef industry. Different models previously developed worldwide are proposed here. Two new computational methodologies that allow the simultaneous selection of the best regression models and the most interesting covariates to predict carcass and/or meat quality are developed. Then, a method of variable clustering is explained that is accurate in evaluating the interrelationships between different parameters of interest. Finally, some principles for the management of quality trade-offs are presented and the Meat Standards Australia model is discussed. The “Pareto front” is an interesting approach to deal jointly with the different sets of expectations and to propose a method that could optimize all expectations together.

Published
2020
Full Text
View/download PDF

2. New Approach Studying Interactions Regarding Trade-Off between Beef Performances and Meat Qualities

Author

Alexandre Conanec, Brigitte Picard, Denis Durand, Gonzalo Cantalapiedra-Hijar, Marie Chavent, Christophe Denoyelle, Dominique Gruffat, Jérôme Normand, Jérôme Saracco, and Marie-Pierre Ellies-Oury

Subjects
trade-off, meat quality, beef performances, modeling, Chemical technology, TP1-1185
Abstract

The beef cattle industry is facing multiple problems, from the unequal distribution of added value to the poor matching of its product with fast-changing demand. Therefore, the aim of this study was to examine the interactions between the main variables, evaluating the nutritional and organoleptic properties of meat and cattle performances, including carcass properties, to assess a new method of managing the trade-off between these four performance goals. For this purpose, each variable evaluating the parameters of interest has been statistically modeled and based on data collected on 30 Blonde d’Aquitaine heifers. The variables were obtained after a statistical pre-treatment (clustering of variables) to reduce the redundancy of the 62 initial variables. The sensitivity analysis evaluated the importance of each independent variable in the models, and a graphical approach completed the analysis of the relationships between the variables. Then, the models were used to generate virtual animals and study the relationships between the nutritional and organoleptic quality. No apparent link between the nutritional and organoleptic properties of meat (r = −0.17) was established, indicating that no important trade-off between these two qualities was needed. The 30 best and worst profiles were selected based on nutritional and organoleptic expectations set by a group of experts from the INRA (French National Institute for Agricultural Research) and Institut de l’Elevage (French Livestock Institute). The comparison between the two extreme profiles showed that heavier and fatter carcasses led to low nutritional and organoleptic quality.

Published
2019
Full Text
View/download PDF

3. ClustOfVar: An R Package for the Clustering of Variables

Author

Marie Chavent, Vanessa Kuentz-Simonet, Benoit Liquet, and Jerome Saracco

Subjects
dimension reduction, hierarchical clustering of variables, k-means clustering of variables, mixture of quantitative and qualitative variables, stability, Statistics, HA1-4737
Abstract

Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing ``as good as possible'' the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two clustering algorithms are proposed to optimize the homogeneity criterion: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets.

Published
2012

15. Certain relationships between Animal Performance, Sensory Quality and Nutritional Quality can be generalized between various experiments on animal of similar types

Author

Jérôme Saracco, Dominique Gruffat, Marie-Pierre Ellies-Oury, Denys Durand, Marie Chavent, Anne Listrat, Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Méthodes avancées d’apprentissage statistique et de contrôle (ASTRAL), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Naval Group, This study was funded by ADEME (Agence de l'Environnement et de la Maîtrise de l'Energie, France)., VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
0301 basic medicine, Nutritional value, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Longissimus Thoracis, Sensory system, Nutritional quality, Biology, 03 medical and health sciences, Carcass weight, Animal performances, medicine, Quality (business), Food science, Flavor, media_common, General Veterinary, 0402 animal and dairy science, food and beverages, 04 agricultural and veterinary sciences, 040201 dairy & animal science, Clustering of variables, Breed, Tenderness, 030104 developmental biology, Meat quality traits, Animal Science and Zoology, medicine.symptom, Fatty acid composition, Prediction
Abstract

International audience; In the beef sector, one of the major challenges is to early predict carcass and meat quality and to jointly satisfy the multiple expectations of the various stakeholders. Thus, the objective of this study was to determine if the relationships among carcass, nutritional and sensory qualities established previously by Ellies-Oury et al. (2016) might be generalized to different type of animals.The Longissimus thoracis muscles of 32 young Charolais bulls were analyzed in terms of sensory and nutritional quality (lipid content and fatty acid composition). These parameters of interest were linked together and to animal performances by using a clustering of variables.Longissimus thoracis sensory and nutritional qualities appear sometimes antagonistic. Indeed, some “positive” sensory descriptors (juiciness, overall appreciation, overall flavor and overall odor) are negatively related to PUFA proportions. PUFA proportions are positively associated with carcass weight but in the same time with rancid/fish flavors. Moreover, CLA and trans MUFA proportions are positively associated with the “negative” descriptors of greasy feel and residues. To finish, carcass weight and ADG are negatively associated with some “positive” sensory descriptors except tenderness.It can be concluded from this work that these relationships, that were already established in previous works, are robust between experiments.In order to highlight robust and generalizable relationships in different contexts, it is now appropriate to apply this method to a larger database containing different traits and various characteristics of breed, slaughter ages, animal types, fattening practices.

Published
2021

16. Has breed any effect on beef sensory quality?

Author

Ian Richardson, S. Failla, John L. Williams, María del Mar Campo, Per Ertbjerg, A. Conanec, M-P. Ellies-Oury, Begoña Panea, Marie Chavent, Jérôme Saracco, J. F. Hocquette, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Zaragoza - Universidad de Zaragoza [Zaragoza], Bristol Veterinary School, University of Helsinki, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Agrifood Research and Technology Centre of Aragon, Av. Monta˜nana, 930, Zaragoza, Spain, Méthodes avancées d’apprentissage statistique et de contrôle (ASTRAL), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Naval Group, Davies Research Centre, University of Adelaide, Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti (DiANA), Università cattolica del Sacro Cuore [Piacenza e Cremona] (Unicatt), Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, and Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA)

Subjects
Marchigiana, [SDV]Life Sciences [q-bio], Sensory system, Sensory analysis, 0404 agricultural biotechnology, Animal science, medicine, Flavor, Mixed effect models, 2. Zero hunger, General Veterinary, biology, 0402 animal and dairy science, food and beverages, Fixed effect models, 04 agricultural and veterinary sciences, biology.organism_classification, 040401 food science, 040201 dairy & animal science, Breed, Tenderness, Lipid content, Animal Science and Zoology, Analysis of variance, medicine.symptom, Beef
Abstract

International audience; A total of 436 young cattle from 15 cattle breeds were reared in as similar conditions as possible to evaluate the impact of breed on sensory quality of beef from longissimus muscle determined by sensory analysis. Two statistical methods for processing the sensory data were compared. The analysis of variance with or without the panelist effect gave similar conclusions indicating that the robustness of the results was not dependent on the method chosen. The 4 meat descriptors (tenderness, juiciness, beef flavor and off-flavor) placed breeds into 5 groups using an unsupervised classification (hierarchical ascending classification). Aberdeen Angus, Highland and Jersey, that have a high lipid content in the muscle studied, differed from the other breeds in that they had a higher beef flavour. The dual-purpose and rustic breeds, Simmental, Casina and Marchigiana, produced significantly less juicy and less tender meat than that from breeds selected for meat production. Overall, despite significant differences previously identified for animal, carcass, muscle and beef traits for the same animals, differences in sensory scores between most of the breeds were small, with only significant differences between the few breeds that had extreme sensory profiles (such as Simmental and Pirenaica).

Published
2021

17. Automatic Detection of Rare Observations During Production Tests Using Statistical Models

Author

Alex Mourer, Madalina Olteanu, Jérôme Lacaille, Marie Chavent, Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM), Université Paris 1 Panthéon-Sorbonne (UP1), Safran Aircraft Engines, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), CEntre de REcherches en MAthématiques de la DEcision (CEREMADE), Centre National de la Recherche Scientifique (CNRS)-Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), This work was supported by the French National Agency for Research and Technology (ANRT) and Safran Aircraft Engines (Safran group), Mourer, Alex, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Paris Dauphine-PSL, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Model selection, Statistical model, Context (language use), 0102 computer and information sciences, General Medicine, computer.software_genre, 01 natural sciences, [STAT] Statistics [stat], [STAT]Statistics [stat], 010104 statistics & probability, 010201 computation theory & mathematics, Unsupervised learning, Anomaly detection, Double check, Data mining, 0101 mathematics, Cluster analysis, computer, Interpretability
Abstract

Engines are verified through production tests before delivering them to customers. During those tests, lot of measures are taken on different parts of the engine, considering multiple physical parameters. Unexpected measures can be observed. For this very reason, it is important to assess if these unusual observations are statistically significant. However, anomaly detection is a difficult problem in unsupervised learning. The obvious reason is that, unlike supervised classification, there is no ground truth against which we could evaluate results. Therefore, we propose a methodology based on two independent statistical algorithms to double check our results. One approach is the Isolation Forest (IF) model which is specific to anomaly detection and able to handle a large number of variables. The goal of the algorithm is to find rare items, events or observations which raise suspicions by differing significantly from the majority of the data and, at the same time, it discriminates non-informative variables to improve. One main issue of IF is its lack of interpretability. Within this scope, we extend the shapley values, interpretation indicators, to the unsupervised context to interpret the model outputs. The second approach is the Self-Organizing Map (SOM) model which has nice properties for data mining by providing both clustering and visual representation. The performance of the method and its interpretability depends on the chosen subset of variables. In this respect, we first implement a sparse-weighted K-means to reduce the input space, allowing the SOM to give an interpretable discretized representation. We apply the two methodologies on data on aircraft engines measurements. Both approaches show similar results which are easily interpretable and exploitable by the experts.

Published
2020

18. Various Statistical Approaches to Assess and Predict Carcass and Meat Quality Traits

Author

Jean-François Hocquette, Linda J. Farmer, Marie Chavent, Alexandre Conanec, Sghaier Chriki, Jérôme Saracco, Marie-Pierre Ellies-Oury, Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agri Food and Biosciences Institute, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
Health (social science), Operations research, Computer science, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Plant Science, lcsh:Chemical technology, Health Professions (miscellaneous), Microbiology, Multi-objective optimization, Article, meat quality, Trrade-o, 0404 agricultural biotechnology, Covariate, Quality (business), lcsh:TP1-1185, Cluster analysis, Selection (genetic algorithm), media_common, trade-off, bovine, 0402 animal and dairy science, Regression analysis, 04 agricultural and veterinary sciences, 040401 food science, 040201 dairy & animal science, meat standards Australia, Variable (computer science), Beef industry, optimization, Food Science, carcass
Abstract

The beef industry is organized around different stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these differing perspectives. Then, various optimization models that might integrate all these expectations are described. The final goal is to define practices that could increase value for animal production, carcasses and meat whilst simultaneously meeting the main expectations of the beef industry. Different models previously developed worldwide are proposed here. Two new computational methodologies that allow the simultaneous selection of the best regression models and the most interesting covariates to predict carcass and/or meat quality are developed. Then, a method of variable clustering is explained that is accurate in evaluating the interrelationships between different parameters of interest. Finally, some principles for the management of quality trade-offs are presented and the Meat Standards Australia model is discussed. The &ldquo, Pareto front&rdquo, is an interesting approach to deal jointly with the different sets of expectations and to propose a method that could optimize all expectations together.

Published
2020

19. Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0

Author

Myriam Khodri, Juliette Mignot, Pablo Ortega, Marie Chavent, Simon Michel, Didier Swingedouw, Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), NCAS-Climate [Reading], Department of Meteorology [Reading], University of Reading (UOR)-University of Reading (UOR), Océan et variabilité du climat (VARCLIM), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), LEFE-IMAGO project VADEMECUM, European Project: 727852,Blue-Action(2016), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)

Subjects
Elastic net regularization, 010504 meteorology & atmospheric sciences, Computer science, lcsh:QE1-996.5, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, 01 natural sciences, Regression, Proxy (climate), Random forest, lcsh:Geology, 010104 statistics & probability, 13. Climate action, North Atlantic oscillation, Partial least squares regression, Paleoclimatology, Statistics, Principal component regression, 0101 mathematics, 0105 earth and related environmental sciences
Abstract

Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.

Published
2020

20. Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness

Author

Jérôme Saracco, Marie-Pierre Ellies-Oury, Brigitte Picard, Alexandre Conanec, Marie Chavent, Muriel Bonnet, Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, European Union (EU) FOODCT-2006-36241European Union (EU), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
0301 basic medicine, Meat, [SDV]Life Sciences [q-bio], lcsh:Medicine, Feature selection, Article, tendrete de la viande, 03 medical and health sciences, 0302 clinical medicine, Linear regression, Statistics, Covariate, Food Quality, Sliced inverse regression, Animals, lcsh:Science, Heat-Shock Proteins, Selection (genetic algorithm), ComputingMilieux_MISCELLANEOUS, Mathematics, Models, Statistical, Multidisciplinary, lcsh:R, Computational Biology, Regression analysis, Statistical model, Regression, Computational biology and bioinformatics, [STAT]Statistics [stat], 030104 developmental biology, Regression Analysis, Cattle, lcsh:Q, Structural biology, biomarqueur, Biomarkers, Metabolic Networks and Pathways, 030217 neurology & neurosurgery
Abstract

In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three regression models (parametric, semi-parametric and non-parametric) are considered and estimated by multiple linear regression, sliced inverse regression and random forests. Both the variables selection and the model choice are computational. A measure of importance based on random perturbations is calculated for each covariate. The variables above a threshold are selected. Then a learning/test samples approach is used to estimate the Mean Square Error and to determine which model (including variable selection) is the most accurate. The R package modvarsel (MODel and VARiable SELection) implements this computational approach and applies to any regression datasets. After checking the good behavior of the methodology on simulated data, the R package is used to select the proteins predictive of meat tenderness among a pool of 21 candidate proteins assayed in semitendinosus muscle from 71 young bulls. The biomarkers were selected by linear regression (the best regression model) to predict meat tenderness. These biomarkers, we confirm the predominant role of heat shock proteins and metabolic ones.

Published
2019

21. New Approach Studying Interactions Regarding Trade-Off between Beef Performances and Meat Qualities

Author

C. Denoyelle, Dominique Gruffat, Denis Durand, Alexandre Conanec, Jérôme Saracco, Marie-Pierre Ellies-Oury, Marie Chavent, Gonzalo Cantalapiedra-Hijar, Brigitte Picard, Jérôme Normand, Université Clermont Auvergne (UCA), Institut National de la Recherche Agronomique (INRA), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Institut de l'élevage (IDELE), Université Grenoble Alpes - UFR Médecine (UGA UFRM), Université Grenoble Alpes (UGA), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), French National Research Agency (ANR) ANR-06-PNRA-018-03, Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
Matching (statistics), Health (social science), 030309 nutrition & dietetics, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Organoleptic, interaction, bœuf, qualité de la viande, Plant Science, Beef cattle, lcsh:Chemical technology, Health Professions (miscellaneous), Microbiology, Article, meat quality, 03 medical and health sciences, Statistics, Added value, Quality (business), lcsh:TP1-1185, ComputingMilieux_MISCELLANEOUS, media_common, Mathematics, modélisation, trade-off, 2. Zero hunger, 0303 health sciences, Variables, business.industry, 0402 animal and dairy science, beef performances, modeling, 04 agricultural and veterinary sciences, 040201 dairy & animal science, [STAT]Statistics [stat], Variable (computer science), Livestock, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Food Science
Abstract

The beef cattle industry is facing multiple problems, from the unequal distribution of added value to the poor matching of its product with fast-changing demand. Therefore, the aim of this study was to examine the interactions between the main variables, evaluating the nutritional and organoleptic properties of meat and cattle performances, including carcass properties, to assess a new method of managing the trade-off between these four performance goals. For this purpose, each variable evaluating the parameters of interest has been statistically modeled and based on data collected on 30 Blonde d&rsquo, Aquitaine heifers. The variables were obtained after a statistical pre-treatment (clustering of variables) to reduce the redundancy of the 62 initial variables. The sensitivity analysis evaluated the importance of each independent variable in the models, and a graphical approach completed the analysis of the relationships between the variables. Then, the models were used to generate virtual animals and study the relationships between the nutritional and organoleptic quality. No apparent link between the nutritional and organoleptic properties of meat (r = &minus, 0.17) was established, indicating that no important trade-off between these two qualities was needed. The 30 best and worst profiles were selected based on nutritional and organoleptic expectations set by a group of experts from the INRA (French National Institute for Agricultural Research) and Institut de l&rsquo, Elevage (French Livestock Institute). The comparison between the two extreme profiles showed that heavier and fatter carcasses led to low nutritional and organoleptic quality.

Published
2019
Full Text
View/download PDF

22. Genome sequencing for rightward hemispheric language dominance

Author

Clyde Francks, Marc Brysbaert, Nathalie Tzourio-Mazoyer, Bernard Mazoyer, Solveig Badillo, Simon E. Fisher, Tulya Kavaklioglu, Lise Van der Haegen, Marie Chavent, Jérôme Saracco, Amaia Carrion-Castillo, Max Planck Institute for Psycholinguistics, Max-Planck-Gesellschaft, Ghent University Hospital, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Universiteit Gent = Ghent University (UGENT), École des hautes études en sciences sociales (EHESS), Donders Institute for Brain, Cognition and Behaviour, Radboud University [Nijmegen], Groupe d'imagerie neurofonctionnelle (GIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Universiteit Gent = Ghent University [Belgium] (UGENT), and Radboud university [Nijmegen]

Subjects
Male, 0301 basic medicine, Social Sciences, LEFT-RIGHT ASYMMETRY, Genome, Functional Laterality, Behavioral Neuroscience, 0302 clinical medicine, Medicine and Health Sciences, Brain asymmetry, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Dominance (genetics), Primary ciliary dyskinesia, next generation sequencing, Genetics, whole genome sequencing, GENE ONTOLOGY, Neurology, laterality, Laterality, functional MRI, Female, Original Article, INTRINSIC CONNECTIVITY, Adult, Neuroinformatics, Genotype, HANDEDNESS, Biology, dominance, Lateralization of brain function, 03 medical and health sciences, medicine, Humans, hemispheric lateralization, HEALTHY, Polymorphism, Genetic, language, Genome, Human, MUTATIONS, [SCCO.NEUR]Cognitive science/Neuroscience, Original Articles, DNA, medicine.disease, Actin cytoskeleton, Situs inversus, 030104 developmental biology, brain asymmetry, PRIMARY CILIARY DYSKINESIA, SITUS-INVERSUS, PATHOGENICITY, 030217 neurology & neurosurgery
Abstract

Most people have left‐hemisphere dominance for various aspects of language processing, but only roughly 1% of the adult population has atypically reversed, rightward hemispheric language dominance (RHLD). The genetic‐developmental program that underlies leftward language laterality is unknown, as are the causes of atypical variation. We performed an exploratory whole‐genome‐sequencing study, with the hypothesis that strongly penetrant, rare genetic mutations might sometimes be involved in RHLD. This was by analogy with situs inversus of the visceral organs (left‐right mirror reversal of the heart, lungs and so on), which is sometimes due to monogenic mutations. The genomes of 33 subjects with RHLD were sequenced and analyzed with reference to large population‐genetic data sets, as well as 34 subjects (14 left‐handed) with typical language laterality. The sample was powered to detect rare, highly penetrant, monogenic effects if they would be present in at least 10 of the 33 RHLD cases and no controls, but no individual genes had mutations in more than five RHLD cases while being un‐mutated in controls. A hypothesis derived from invertebrate mechanisms of left‐right axis formation led to the detection of an increased mutation load, in RHLD subjects, within genes involved with the actin cytoskeleton. The latter finding offers a first, tentative insight into molecular genetic influences on hemispheric language dominance., Study design of the genome sequencing for rightward hemispheric language dominance. Images are shown from an example subject with typical left‐hemisphere language dominance, and an example subject with atypical rightward hemispheric language dominance, as assessed by functional magnetic resonance imaging. Genomic analysis was focused on rare, protein‐altering variants within genes and candidate gene‐sets.

Published
2019

23. Classification de variables et analyse multivariée de données mixtes issues d’une étude BCI

Author

Ricardo Ron-Angevin, Jérôme Saracco, Véronique Lespinet-Najib, Marie Chavent, Liliana Audin-Garcia, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, COGNITIQUE, Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1-Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, and Universidad de Málaga [Málaga] = University of Málaga [Málaga]

Subjects
Computer science, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Multivariate statistical, Cluster analysis, Humanities, Categorical variable, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

International audience; L'objectif de ce travail est de traiter des données complexes issues de la technique des Brain Computer Interfaces (BCI) au moyen de méthodes statistiques multivariées (approche PCAmix et classification de variables) afin de mieux comprendre et interpréter les relations qui existent entre elles. Cet article présente ainsi la classification de variables qui a pour but de réunir des variables fortement liées entre elles. L'approche proposée fonctionne avec des données mixtes, c'est à dire des données contenant des variables numériques et des variables catégorielles. Deux algorithmes de classification de variables sont décrits : un de classification hiérarchique et un autre de partitionnement de type k-means. Une rapide description de la méthode PCAmix (qui permet de faire de l'analyse en composantes principales pour des données mixtes) est fournie, vu que le calcul des variables synthétiques résumant les classes de variables obtenues est fondé sur cette méthode multivariée. Enfin, les approches PCAmix et ClustOfVar (implémentées dans les packages R ClustOfVar et PCAmixdata) sont mises en oeuvre sur les données réelles issues de l'étude BCI. Des recommandations, reposant non seulement sur des critères de performances, d'efficience mais aussi de satisfaction, ont pu être faites concernant le choix d'interface dans l'usage des claviers virtuels, notamment pour des personnes avec des problèmes moteurs tels que la maladie de Charcot. ABSTRACT. The aim of this work is to analyze complex data from a Brain Computer Interfaces (BCI) study using multivariate statistical methods (PCAmix approach and clustering of variables) to better understand and interpret their relationships. This article presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described : a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the PCAmix and ClustOfVar approaches (implemented in the R packages ClustOfVar and PCAmixdata) are illustrated on a real dataset from a BCI (brain computer interface) study. Recommendations, based not only on performance, efficiency, but also on satisfaction criteria, could be made concerning the choice of interface in the use of virtual keyboards, especially for people with motor disorder such as Charcot's disease. MOTS-CLÉS. Classification de variables, données mixtes, analyse en composantes principales, packages R, brain computer interface, analyse multivariée des données, visualisation des données. KEYWORDS. clustering of variables, mixed data, principal component analysis for mixed data, R packages, brain computer interface, multivariate data analysis, data visualization.

Published
2018

24. Clustering of Variables for Mixed Data

Author

Marie Chavent and Jérôme Saracco

Subjects
Physics, Multivariate statistics, Dimensionality reduction, 05 social sciences, General Engineering, Astronomy and Astrophysics, computer.software_genre, 01 natural sciences, Hierarchical clustering, Data set, 010104 statistics & probability, Space and Planetary Science, 0502 economics and business, Principal component analysis, Consensus clustering, Data mining, 0101 mathematics, Cluster analysis, Categorical variable, computer, 050205 econometrics
Abstract

This chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages ClustOfVar and PCAmixdata are illustrated on real mixed data. The PCAmix and ClustOfVar approaches are first used for dimension reduction (step 1) before applying in step 2 a standard clustering method to obtain groups of individuals.

Published
2016

25. Reconstructing climatic modes of variability from proxy records: sensitivity to the methodological approach

Author

Pablo Ortega, Didier Swingedouw, Simon Michel, Myriam Khodri, Marie Chavent, and Juliette Mignot

Subjects
010504 meteorology & atmospheric sciences, business.industry, Computer science, Big data, Mode (statistics), 020206 networking & telecommunications, 02 engineering and technology, Modular design, computer.software_genre, 01 natural sciences, Cross-validation, Toolbox, Proxy (climate), Correlation, 0202 electrical engineering, electronic engineering, information engineering, Principal component regression, Data mining, business, computer, 0105 earth and related environmental sciences
Abstract

Modes of climate variability strongly impact our climate and thus human society. Nevertheless, their statistical properties remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is a useful way to improve our understanding of their behaviours and meteorological impacts. For doing so, several statistical reconstruction methods exist, among which the Principal Component Regression is one of the most widely used. Additional predictive, and then reconstructive, statistical methods have been developed recently, following the advent of big data. Here, we provide to the climate community a multi-statistical toolbox, based on four statistical learning methods and cross validation algorithms, that enables systematic reconstruction of any climate mode of variability as long as there are proxy records that overlap in time with the observed variations of the considered mode. The efficiency of the methods can vary, depending on the statistical properties of the mode and the learning set, thereby allowing to assess sensitivity related to the reconstruction techniques. This toolbox is modular in the sense that it allows different inputs like the proxy database or the chosen variability mode. As an example, the toolbox is here applied to the reconstruction of the North Atlantic Oscillation (NAO) by using Pages 2K database. In order to identify the most reliable reconstruction among those given by the different methods, we also investigate the sensitivity to the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the reconstruction period targeted. The best reconstruction of the NAO that we thus obtain shows significant correlation with former reconstructions, but exhibits better validation scores.

Published
2018

26. Identification des cas de trépanations dans les populations anciennes : base de données et outil interactif de soutien au diagnostic différentiel

Author

Emilie Dodré, Maryelle Bessou, Marie Chavent, Aline Thomas, Caroline Partiot, Bruno Maureille, De la Préhistoire à l'Actuel : Culture, Environnement et Anthropologie (PACEA), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), Eco-Anthropologie et Ethnobiologie (EAE), and Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Université Paris Diderot - Paris 7 (UPD7)

Subjects
Cultural Studies, Archeology, 060101 anthropology, History, 060102 archaeology, Anthropology, Perforation (oil well), 0601 history and archaeology, 06 humanities and the arts, Humanities, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

Les nombreux cas de trépanations crâniennes dans les populations anciennes sont identifiés très majoritairement à partir de la forme de l'ouverture pratiquée sur la voûte. En l'absence de traces caractéristiques des outils du praticien, il est toutefois toujours possible qu'il s'agisse d'une «pseudo-trépanation», c'est-à-dire d'un orifice d'origine pathologique ou taphonomique mimant l'allure d'une ouverture anthropique. Notre étude a pour but d'améliorer le travail de comparaison du diagnostic différentiel grâce à la création d'une base de données recensant les multiples facteurs responsables de lacunes de la voûte crânienne. Ce référentiel a permis l'élaboration d'un outil interactif de soutien au diagnostic différentiel sur la plateforme collaborative de gestion de données Xper3, la première de ce type. Notre revue bibliographique de la littérature anthropologique et médicale a rassemblé dans une base de données les caractéristiques morphologiques des lacunes produites par 79 étiologies en fonction de 13 critères de description quantitatifs et qualitatifs. La comparaison d'un cas archéologique avec des cas connus par l'intermédiaire de l'outil interactif facilite alors le travail de recherches comparatives, oriente l'observateur vers des étiologies potentiellement compatibles et consolide le diagnostic différentiel en augmentant rapidement le nombre de facteurs à discuter.

Published
2017

27. ClustGeo: an R package for hierarchical clustering with spatial constraints

Author

Jérôme Saracco, Marie Chavent, Amaury Labenne, Vanessa Kuentz-Simonet, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Environnement, territoires et infrastructures (UR ETBX), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
0301 basic medicine, Statistics and Probability, Discrete mathematics, FOS: Computer and information sciences, Feature vector, Homogeneity (statistics), Machine Learning (stat.ML), [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], 01 natural sciences, Statistics - Computation, Hierarchical clustering, 010104 statistics & probability, 03 medical and health sciences, Computational Mathematics, Matrix (mathematics), R package, 030104 developmental biology, Statistics - Machine Learning, Convex combination, 0101 mathematics, Statistics, Probability and Uncertainty, [STAT.CO]Statistics [stat]/Computation [stat.CO], Computation (stat.CO), Mathematics
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM-QT2-ADAPTATION [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The first matrix gives the dissimilarities in the "feature space" and the second matrix gives the dissimilarities in the "constraint space". The criterion minimized at each stage is a convex combination of the homogeneity criterion calculated with $D_0$ and the homogeneity criterion calculated with $D_1$. The idea is then to determine a value of $\alpha$ which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest i.e. those of the feature space. This procedure is illustrated on a real dataset using the R package ClustGeo.

Published
2017

28. Une nouvelle approche méthodologique pour piloter la conduite en élevage

Author

Marie-Pierre Ellies, Gonzalo Cantalapiedra Hijar, Denis Durand, Dominique Gruffat, Anne Listrat, Didier Micol, Isabelle Ortigues Marty, Jean-François Hocquette, Marie Chavent, Jerôme Saracco, Brigitte Picard, Unité Mixte de Recherches sur les Herbivores ( UMR 1213 Herbivores ), VetAgro Sup ( VAS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique ( INRA ), Institut de Mathématiques de Bordeaux ( IMB ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ), Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine ( Bordeaux Sciences Agro ), Quality control and dynamic reliability ( CQFD ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)

Subjects
analyse de données, pilotage, conduite d'élevage, statistical method, regroupement de variable, clustOfVar, méthode statistique, [ SDV ] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], data analysis, livestock management
Abstract

Cet article fait référence à une étude publiée en fin d’année 2016 dans la revue Meat Science (122, 163-172). Afin de trouver un compromis entre les attentes des producteurs, des transformateurs et des consommateurs, les auteurs ont cherché à déterminer dans quelle mesure il est possible de piloter les performances des animaux par le biais des pratiques d’élevage, tout en préservant les qualités sensorielles et la valeur santé des viandes. Pour cela, une méthode statistique innovante a été utilisée : la classification de variables. Les données de 71 taurillons du projet européen ProSafeBeef ont été organisées en 3 jeux de données caractérisant respectivement les performances animales (36 variables), les qualités sensorielles (29 variables) et la valeur nutritionnelle des viandes (32 variables). Par la construction de 5 index synthétiques pour chaque élément du triptyque (combinaisons linéaires d’un sous-ensemble de variables constituant un pool homogène), la méthode a permis de supprimer l’information redondante et ainsi de réduire l’information des 97 variables en 15 index synthétiques. Un second regroupement de variables a ensuite été réalisé sur ces 15 index synthétiques afin de mettre en évidence les proximités entre les 3 jeux de données. L’analyse a montré qu’il n’existe pas d’opposition entre efficience animale et valeur nutritionnelle des viandes. Il est donc possible d’obtenir des animaux efficients (c’est-à-dire avec de meilleurs gains de poids et d’efficacité alimentaire) associant des carcasses à haute valeur bouchère (correspondant à un meilleur développement musculaire) et des viandes relativement riches en acides gras polyinsaturés. En revanche, l‘efficience alimentaire et le développement musculaire sont apparus négativement corrélés avec les qualités sensorielles des viandes (notamment la tendreté et l’appréciation globale). La classification de variables est une méthode d’analyse de données fréquemment employée dans certains domaines mais elle n’avait jamais été utilisée en sciences animales. D’après les résultats obtenus, cette approche pourrait s’avérer être un outil efficace pour intégrer différents concepts dans un pilotage optimisé de l’élevage pour les productions animales., This work initially published in Meat Science (122, 163-172) in 2016 provides a new methodological approach to establish the links between different data sets, by using a variable clustering procedure instead of a classical individual clustering approach. Three data sets (animal performance, nutritional value of the meat, sensory quality traits of meat) were considered for a total of 97 variables for each of the 71 young bulls. Variables of each dataset were arranged into homogeneous synthetic variables. Then, the three pools of synthetic variables were analysed together to establish the links existing among the three data sets. Classification showed no opposition between animal performances and nutritional value of meat, as it seemed possible to identify animals with a high butcher value and intramuscular fat relatively rich in polyunsaturated fatty acids. However, the classification indicated that animal performances were negatively correlated with sensory quality of meat. This method appeared to be a useful contribution to the management of animal breeding for an optimal trade-off between the three elements of the triptych (animal performances, nutritional value and sensory quality of meat).

Published
2017

29. Combining clustering of variables and feature selection using random forests

Author

Robin Genuer, Marie Chavent, Jérôme Saracco, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Genuer, Robin, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, Clustering high-dimensional data, Variable selection, 0211 other engineering and technologies, Feature selection, Mathematics - Statistics Theory, 02 engineering and technology, Statistics Theory (math.ST), computer.software_genre, 01 natural sciences, 010104 statistics & probability, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics, FOS: Mathematics, 0101 mathematics, Cluster analysis, Categorical variable, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Selection (genetic algorithm), Mathematics, 021103 operations research, Dimensionality reduction, Random forests, Clustering of variables, Hierarchical clustering, Random forest, High-dimensional data, ComputingMethodologies_PATTERNRECOGNITION, Modeling and Simulation, Supervised classification, Data mining, computer
Abstract

Standard approaches to tackle high-dimensional supervised classification problem often include variable selection and dimension reduction procedures. The novel methodology proposed in this paper combines clustering of variables and feature selection. More precisely, hierarchical clustering of variables procedure allows to build groups of correlated variables in order to reduce the redundancy of information and summarizes each group by a synthetic numerical variable. Originality is that the groups of variables (and the number of groups) are unknown a priori. Moreover the clustering approach used can deal with both numerical and categorical variables (i.e. mixed dataset). Among all the possible partitions resulting from dendrogram cuts, the most relevant synthetic variables (i.e. groups of variables) are selected with a variable selection procedure using random forests. Numerical performances of the proposed approach are compared with direct applications of random forests and variable selection using random forests on the original p variables. Improvements obtained with the proposed methodology are illustrated on two simulated mixed datasets (cases $n > p$ and $n < p$, where n is the sample size) and on a real proteomic dataset. Via the selection of groups of variables (based on the synthetic variables), interpretability of the results becomes easier.

Published
2016

30. An innovative approach combining Animal Performances, nutritional value and sensory quality of meat

Author

Jérôme Saracco, Jean-François Hocquette, Gonzalo Cantalapiedra-Hijar, Marie Chavent, Brigitte Picard, Anne Listrat, Isabelle Ortigues-Marty, Denys Durand, Didier Micol, Dominique Gruffat, Marie-Pierre Ellies-Oury, Bordeaux Sciences Agro [Gradignan], Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, EU FP6 Integrated Project ProSafeBeef FOODCT-2006-36241, European Union, Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine ( Bordeaux Sciences Agro ), Unité Mixte de Recherches sur les Herbivores ( UMR 1213 Herbivores ), VetAgro Sup ( VAS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique ( INRA ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Quality control and dynamic reliability ( CQFD ), Institut de Mathématiques de Bordeaux ( IMB ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
0301 basic medicine, Male, Animal breeding, [ STAT ] Statistics [stat], Nutritional value, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Sensory system, Biology, 03 medical and health sciences, Animals, Humans, Quality (business), Animal Husbandry, Muscle, Skeletal, media_common, Principal Component Analysis, 030109 nutrition & dietetics, [ SDV ] Life Sciences [q-bio], business.industry, Animal Performances, 0402 animal and dairy science, 04 agricultural and veterinary sciences, 040201 dairy & animal science, Clustering of variables, Biotechnology, [STAT]Statistics [stat], Red Meat, Meat quality traits, Homogeneous, Taste, Principal component analysis, Fatty Acids, Unsaturated, Cattle, Fatty acid composition, Intramuscular fat, business, Value (mathematics), Nutritive Value, Food Science
Abstract

International audience; This work sets out a methodological approach to assess how to simultaneously control together Animal Performances,nutritional value, sensory quality of meat. Seventy-one young bulls were characterized by 97 variables.Variables of each element were arranged into either 5 homogeneous Intermediate Scores (IS) or 2 Global Indices(GI) via a clustering of variables and analysed together by Principal Component Analysis (PCA). These 3 pools of 5IS (or 2 GI) were analysed together by PCA to established the links existing among the triptych. Classification onIS showed no opposition between Animal Performances and nutritional value of meat, as it seemed possible toidentify animals with a high butcher value and intramuscular fat relatively rich in polyunsaturated fatty acids.Concerning GI, the classification indicated that Animal Performances were negatively correlated with sensoryquality. This method appeared to be a useful contribution to the management of animal breeding for an optimaltrade-off between the three elements of the triptych.

Published
2016

31. Les relations entre la tendreté et la composition en acides gras des viandes bovines sont elles stables selon le sexe et la race ?

Author

Marie-Pierre Ellies, Denis Durand, Dominique Gruffat, Anne Listrat, Didier Micol, Isabelle Ortigues Marty, Jean-François Hocquette, Marie Chavent, Saracco, J., Brigitte Picard, ProdInra, Archive Ouverte, Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Unité Mixte de Recherches sur les Herbivores ( UMR 1213 Herbivores ), VetAgro Sup ( VAS ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique ( INRA ), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine ( Bordeaux Sciences Agro ), Institut de Mathématiques de Bordeaux ( IMB ), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
[SDV] Life Sciences [q-bio], tenderness, [ SDV ] Life Sciences [q-bio], viande bovine, [SDV]Life Sciences [q-bio], qualité sensorielle, composition en acide gras, race bovine, beef, tendrete
Abstract

Regarding beef meat, consumers have specific expectations especially concerning meat quality traits (tenderness and tasteespecially) but also nutritional value of meat (fatty acid composition in particular). Thus, the aim of the present work was todetermine to what extent the choice of animal breed and type will meet the consumer’s expectations.The study concerned 75 young bulls from three different breeds (Blond d’Aquitaine, Limousin, Angus) and 13 Charolaisheifers.Overall, the oxidative metabolism appears associated with higher tenderness but lower nutritional value of meat (lesspolyunsaturated fatty acids, more saturated ones). However, for a given breed / a given type of animal, it is difficult toassociate clearly a kind of fatty acid composition and a level of tenderness, letting us suppose that a trade-off has to beestablished, in order to satisfy the various consumers’ expectations.

Published
2016

32. A Sliced Inverse Regression Approach for a Stratified Population

Author

Benoit Liquet, Vanessa Kuentz, Marie Chavent, and Jérôme Saracco

Subjects
Statistics and Probability, education.field_of_study, Heteroscedasticity, 05 social sciences, Population, Estimator, Sufficient dimension reduction, Regression analysis, 01 natural sciences, 010104 statistics & probability, Homoscedasticity, 0502 economics and business, Statistics, Sliced inverse regression, 0101 mathematics, education, Categorical variable, 050205 econometrics, Mathematics
Abstract

In this article, we consider a semiparametric single index regression model involving a real dependent variable Y, a p-dimensional quantitative covariable X, and a categorical predictor Z which defines a stratification of the population. This model includes a dimension reduction of X via an index X′β. We propose an approach based on sliced inverse regression in order to estimate the space spanned by the common dimension reduction direction β. We establish -consistency of the proposed estimator and its asymptotic normality. Simulation study shows good numerical performance of the proposed estimator in homoscedastic and heteroscedastic cases. Extensions to multiple indices models, q-dimensional response variable, and/or SIRα-based methods are also discussed. The case of unbalanced subpopulations is treated. Finally, a practical method to investigate if there is or not a common direction β is proposed.

Published
2011

33. On Central Tendency and Dispersion Measures for Intervals and Hypercubes

Author

Jérôme Saracco, Marie Chavent, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Groupe de Recherche en Economie Théorique et Appliquée (GREThA), Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, 02 engineering and technology, Interval (mathematics), Hausdorff Distance, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Statistics - Computation, 01 natural sciences, Summary statistics, Clustering, 010104 statistics & probability, Statistics - Machine Learning, Statistics, 0202 electrical engineering, electronic engineering, information engineering, Multidimensional Interval Data, 020201 artificial intelligence & image processing, Statistical dispersion, Hypercube, 0101 mathematics, [STAT.CO]Statistics [stat]/Computation [stat.CO], Value (mathematics), Mathematics
Abstract

The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of summary statistics (central tendency and dispersion measures) for interval-valued variables.

Published
2008

34. A methodological approach to assess the interactions between animal performances, nutritional value and sensory quality of meat

Author

Marie-Pierre Ellies, Gonzalo Cantalapiedra Hijar, Denis Durand, Dominique Gruffat, Anne Listrat, Didier Micol, Isabelle Ortigues Marty, Jean-François Hocquette, Marie Chavent, Saracco, J., Brigitte Picard, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bordeaux (UB), and VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, étude méthodologique, valeur nutritionnelle des aliments, qualité sensorielle, qualité de la viande, performance animale, Agricultural sciences, piloting animal breeding, régime alimentaire, viande bovine, Alimentation et Nutrition, taurillon, Food and Nutrition, clustering of variables, meat quality traits, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Sciences agricoles
Abstract

National audience; Animal performances (including feed efficiency), nutritional value and sensory quality of meat were characterized for young bulls of 3 breeds and 4 feeding regimes. Variables of each element of this triptych were arranged into homogeneous clusters in order to constitute different synthetic quantitative variable, which were further combined in Global Indexes. If the animal performances Global Index (GI) appears negatively correlated to the Sensory one, the Nutritional GI could be either positively or negatively correlated to the Sensory GI, depending on the way of expressing the fatty acid composition of meat. This method allows clearly discriminating breeds and feeding regimes in terms of the studied variables, and appears useful to contribute to pilot animal breeding for a better tradeoff management in the bovine sector.

Published
2015

35. Adaptive Hausdorff distances and dynamic clustering of symbolic interval data

Author

Renata M. C. R. de Souza, Yves Lechevallier, Francisco de A. T. de Carvalho, Marie Chavent, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Usage-centered design, analysis and improvement of information systems (AxIS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), INRIA Rocquencourt, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Rand index, Single-linkage clustering, 02 engineering and technology, Interval (mathematics), [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], 01 natural sciences, Symbolic data analysis, 010104 statistics & probability, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, interval data, 0202 electrical engineering, electronic engineering, information engineering, adaptive distances, 0101 mathematics, Cluster analysis, k-medians clustering, Mathematics, k-medoids, Hausdorff distance, dynamic clustering, ComputingMethodologies_PATTERNRECOGNITION, Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Algorithm, Software
Abstract

This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocation algorithms involving the construction of the clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between the clusters and their corresponding representatives. In this paper, each pattern is represented by a vector of intervals. Adaptive Hausdorff distances are the measures used to compare two interval vectors. Adaptive distances at each iteration change for each cluster according to its intra-class structure. The advantage of these adaptive distances is that the clustering algorithm is able to recognize clusters of different shapes and sizes. To evaluate this method, experiments with real and synthetic interval data sets were performed. The evaluation is based on an external cluster validity index (corrected Rand index) in a framework of a Monte Carlo experiment with 100 replications. These experiments showed the usefulness of the proposed method.

Published
2006

36. Feature extraction and classification of EEG signals. The use of a genetic algorithm for an application on alertness prediction

Author

Marie Chavent, Pierrick Legrand, Frédérique Faïta-Aïnseba, Leonardo Trujillo, Laurent Vezard, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB), Instituto Tecnológico de Tijuana [Tijuana], Eduardo Miranda, Julien Castet, Benjamin Knapp, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]

Subjects
Computer science, Feature extraction, 02 engineering and technology, Electroencephalography, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Linear regression, 0202 electrical engineering, electronic engineering, information engineering, medicine, Preprocessor, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, Regression, Random forest, Alertness, Binary classification, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

This chapter presents a method to automatically determine the alertness state of humans. Such a task is relevant in diverse domains, where a person is expected or required to be in a particular state of alertness. For instance, pilots, security personnel, or medical personnel are expected to be in a highly alert state, and this method could help to confirm this or detect possible problems. In this work, electroencephalographic (EEG) data from 58 subjects in two distinct vigilance states (state of high and low alertness) was collected via a cap with 58 electrodes. Thus, a binary classification problem is considered. To apply the proposed approach in a real-world scenario, it is necessary to build a prediction method that requires only a small number of sensors (electrodes), minimizing the total cost and maintenance of the system while also reducing the time required to properly setup the EEG cap. The approach presented in this chapter applies a preprocessing method for EEG signals based on the use of discrete wavelet decomposition (DWT) to extract the energy of each frequency in the signal. Then, a linear regression is performed on the energies of some of these frequencies and the slope of this regression is retained. A genetic algorithm (GA) is used to optimize the selection of frequencies on which the regression is performed and to select the best recording electrode. Results show that the proposed strategy derives accurate predictive models of alertness.

Published
2014

37. A sliced inverse regression approach for data stream

Author

Jérôme Saracco, Marie Chavent, Vanessa Kuentz-Simonet, Thi Mong Ngoc Nguyen, Benoit Liquet, Stéphane Girard, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Aménités et dynamiques des espaces ruraux (UR ADBX), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche Mathématique Avancée (IRMA), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Girard, Stephane, Institut de Mathématiques de Bordeaux ( IMB ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ), Quality control and dynamic reliability ( CQFD ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Modelling and Inference of Complex and Structured Stochastic Systems ( MISTIS ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), Aménités et dynamiques des espaces ruraux ( UR ADBX ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut de Recherche Mathématique Avancée ( IRMA ), and Université de Strasbourg ( UNISTRA ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Statistics and Probability, Data stream, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Estimator, Asymptotic distribution, computer.software_genre, Effective dimension, Effective dimension reduction (EDR), Computational Mathematics, [ STAT.ME ] Statistics [stat]/Methodology [stat.ME], Consistency (statistics), Sliced inverse regression (SIR), Sliced inverse regression, Data mining, Semiparametric regression, Statistics, Probability and Uncertainty, computer, Algorithm, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Block (data storage), Mathematics
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM-QT2-ADAPTATION; International audience; In this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric regression model involving a common EDR (Effective Dimension Reduction) direction is assumed in each block. Our goal is to estimate this direction at each arrival of a new block. A simple direct approach consists of pooling all the observed blocks and estimating the EDR direction by the SIR (Sliced Inverse Regression) method. But in practice, some disadvantages appear such as the storage of the blocks and the running time for large sample sizes. To overcome these drawbacks, we propose an adaptive SIR estimator of based on the optimization of a quality measure. The corresponding approach is faster both in terms of computational complexity and running time, and provides data storage benefits. The consistency of our estimator is established and its asymptotic distribution is given. An extension to multiple indices model is proposed. A graphical tool is also provided in order to detect changes in the underlying model, i.e., drift in the EDR direction or aberrant blocks in the data stream. A simulation study illustrates the numerical behavior of our estimator. Finally, an application to real data concerning the estimation of physical properties of the Mars surface is presented.

Published
2014

38. EEG classification for the detection of mental states

Author

Leonardo Trujillo, Pierrick Legrand, Laurent Vezard, Marie Chavent, Frédérique Faïta-Aïnseba, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2, Instituto Tecnológico de Tijuana [Tijuana], ACOBSEC IRSES FP7, Université de Bordeaux (UB), European Project: 612689,EC:FP7:PEOPLE,FP7-PEOPLE-2013-IRSES,ACOBSEC(2013), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]

Subjects
medicine.diagnostic_test, business.industry, Computer science, [SDV]Life Sciences [q-bio], Pattern recognition, Electroencephalography, Machine learning, computer.software_genre, Contingent negative variation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT]Statistics [stat], Alertness, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], medicine, [INFO]Computer Science [cs], [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, [MATH]Mathematics [math], business, computer, Software, ComputingMilieux_MISCELLANEOUS
Abstract

Graphical abstractDisplay Omitted HighlightsA method able to automatically determine mental states of alertness is developed.A complete protocol of EEG data acquisition is detailed.Analysis of the Contingent Negative Variation is used to validate the EEG data.Best compromise number of electrodes/quality of the solution: 9 electrodes.Provides an accurate and fast prediction of the alertness of an unseen individual. The objective of the present work is to develop a method that is able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state of mind. For instance, pilots and medical staff are expected to be in a highly alert state and the proposed method could help to detect possible deviations from this expected state. This work poses a binary classification problem where the goal is to distinguish between a "relaxed" state and a baseline state ("normal") from the study of electroencephalographic signals (EEG) collected with a small number of electrodes. The EEG of 58 subjects in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select a subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the subjects. Different subset sizes were investigated and the best compromise between the number of selected electrodes and the quality of the solution was obtained by considering 9 electrodes. Even if the present approach is costly in computation time (GA search), it allows to construct a decision rule that provides an accurate and fast prediction of the alertness state of an unseen individual.

Published
2014

39. Taxonomy of gymnospermae: multivariate analyses of leaf fatty acid composition

Author

Sébastien Mongrand, Brigitte Patouille, Chantal Lacomblez, Claude Cassagne, Marie Chavent, Jean-Jacques Bessoule, and Alain Badoc

Subjects
Chloroplasts, Larix, Plant Science, Horticulture, Biochemistry, Cedrus, Stangeriaceae, Terminology as Topic, Botany, Molecular Biology, Phylogeny, biology, Galactolipids, Fatty Acids, Discriminant Analysis, Zamiaceae, General Medicine, Pinaceae, biology.organism_classification, Lipids, Ginkgoaceae, Plant Leaves, Cycadopsida, Cephalotaxaceae, Multivariate Analysis, Glycolipids, Taxaceae, Podocarpaceae, Abies
Abstract

The fatty acid composition of photosynthetic tissues from 137 species of gymnosperms belonging to 14 families was determined by gas chromatography. Statistical analysis clearly discriminated four groups. Ginkgoaceae, Cycadaceae, Stangeriaceae, Zamiaceae, Sciadopityaceae, Podocarpaceae, Cephalotaxaceae, Taxaceae, Ephedraceae and Welwitschiaceae are in the first group, while Cupressaceae and Araucariaceae are mainly in the second one. The third and the fourth groups composed of Pinaceae species are characterized by the genera Larix, and Abies and Cedrus, respectively. Principal component and discriminant analyses and divisive hierarchical clustering analysis of the 43 Pinaceae species were also performed. A clear-cut separation of the genera Abies, Larix, and Cedrus from the other Pinaceae was evidenced. In addition, a mass analysis of the two main chloroplastic lipids from 14 gymnosperms was performed. The results point to a great originality in gymnosperms since in several species and contrary to the angiosperms, the amount of digalactosyldiacylglycerol exceeds that of monogalactosyldiacylglycerol.

Published
2001

40. A monothetic clustering method

Author

Marie Chavent

Subjects
Fuzzy clustering, business.industry, Correlation clustering, Single-linkage clustering, Pattern recognition, Hierarchical clustering, ComputingMethodologies_PATTERNRECOGNITION, Artificial Intelligence, Signal Processing, FLAME clustering, Computer Vision and Pattern Recognition, Artificial intelligence, Hierarchical clustering of networks, business, Cluster analysis, Software, k-medians clustering, Mathematics
Abstract

The proposed divisive clustering method performs simultaneously a hierarchy of a set of objects and a monothetic characterization of each cluster of the hierarchy. A division is performed according to the within-cluster inertia criterion which is minimized among the bipartitions induced by a set of binary questions. In order to improve the clustering, the algorithm revises at each step the division which has induced the cluster chosen for division.

Published
1998

41. Detecting mental states of alertness with genetic algorithm variable selection

Author

Leonardo Trujillo, Marie Chavent, Laurent Vezard, Frédérique Faïta-Aïnseba, Pierrick Legrand, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2, Instituto Tecnológico de Tijuana, Instituto Tecnológico de Tijuana [Tijuana], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Instituto Tecnológico de Tijuana = Tijuana Institute of Technology [Tijuana]

Subjects
Computer science, media_common.quotation_subject, Feature selection, 02 engineering and technology, Electroencephalography, Machine learning, computer.software_genre, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, medicine, media_common, medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 020206 networking & telecommunications, Pattern recognition, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Alertness, 020201 artificial intelligence & image processing, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Vigilance (psychology)
Abstract

International audience; Email Print Request Permissions Save to Project The objective of the present work is to develop a method able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. For instance, pilots or medical staffs are expected to be in a highly alert state, and this method could help to detect possible problems. In this paper, an approach is developed to predict the state of alertness ("normal" or "relaxed") from the study of electroencephalographic signals (EEG) collected with a limited number of electrodes. The EEG of 58 participants in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select an optimal subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the participants. Different subset sizes were investigated and the best result was obtained by considering 9 electrodes (correct classification rate of 73.68%).

Published
2013

42. Régression inverse par tranches sur flux de données

Author

Marie Chavent, Stéphane Girard, Vanessa Kuentz, Benoit Liquet, Thi Mong Ngoc Nguyen, Jérôme Saracco, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Aménités et dynamiques des espaces ruraux (UR ADBX), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche Mathématique Avancée (IRMA), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Irstea, Import Ws, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG)

Subjects
[SDE] Environmental Sciences, analyse de données, analyse statistique, [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], flux de données, [SDE]Environmental Sciences, méthode d'analyse, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Sliced Inverse Regression
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAMorganisées par la Société Française de Statistique; National audience; Dans cette communication, nous nous concentrons sur des données arrivant séquentiellement par bloc. Nous supposons la présence d'un modèle semi-paramétrique sous-jacent incluant une direction EDR (Effective Dimension Reduction) commune dans chaque bloc. Nous proposons une approche SIR (Sliced Inverse Regression pour régression inverse par tranches) adaptative afin d'estimer b. L'estimateur proposé est plus rapide qu'une application séquentielle de la méthode SIR à l'union des blocs disponibles. Nous montrons la convergence en probabilité et la normalité asymptotique de cet estimateur. Dans une simulation, nous illustrons le bon comportement numérique de notre approche. Nous fournissons également des graphiques permettant de détecter s'il existe une dérive de la direction EDR ou bien des blocs de données aberrantes, et nous illustrons notre approche avec différents scénarios. Quelques extensions possibles de cette méthode sont discutées en conclusion. / In this communication, we consider block-wise evolving data streams. When a semiparametric regression model involving a common dimension reduction direction is assumed for each block, we propose an adaptive SIR (Sliced Inverse Regression) estimator of B. This estimator is faster than usual SIR applied to the union of all the blocks, both from computational complexity and running time points of view. We show the consistency of our estimator at the root-n rate and give its asymptotic distribution. In a simulation study, we illustrate the good numerical behavior of the estimator. We also provide a graphical tool in order to detect if there exists a drift of the dimension reduction direction or some aberrant blocks of data. We illustrate our approach with various scenarios. Finally, possible extensions of this method are given.

Published
2012

43. Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

Author

François Husson, Marie Chavent, Julie Josse, Benoit Liquet, Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Quality control and dynamic reliability ( CQFD ), Institut de Mathématiques de Bordeaux ( IMB ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Université Bordeaux Segalen - Bordeaux 2, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux ( UB ) -Institut Polytechnique de Bordeaux ( Bordeaux INP ) -Centre National de la Recherche Scientifique ( CNRS ), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Chavent, Marie

Subjects
Multivariate statistics, Library and Information Sciences, Overfitting, computer.software_genre, [STAT.CO] Statistics [stat]/Computation [stat.CO], 01 natural sciences, 010104 statistics & probability, Mathematics (miscellaneous), 0504 sociology, Multiple correspondence analysis, Imputation (statistics), 0101 mathematics, [STAT.CO]Statistics [stat]/Computation [stat.CO], Categorical variable, [ STAT.CO ] Statistics [stat]/Computation [stat.CO], Mathematics, Homogeneity (statistics), 05 social sciences, 050401 social sciences methods, Missing data, Exploratory data analysis, Psychology (miscellaneous), Data mining, Statistics, Probability and Uncertainty, computer, Algorithm
Abstract

International audience; A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function over all non-missing elements. This can be achieved by EM-type algorithms where an iterative imputation of the missing values is performed during the estimation of the axes and components. This paper proposes such an algorithm, named iterative multiple correspondence analysis, to handle missing values in multiple correspondence analysis (MCA). This algorithm, based on an iterative PCA algorithm, is described and its properties are studied. We point out the over tting problem and propose a regularized version of the algorithm to overcome this major issue. Finally, performances of the regularized iterative MCA algorithm (implemented in the R-package named missMDA) are assessed from both simulations and a real dataset. Results are promising with respect to other methods such as the missing-data passive modi ed margin method, an adaptation of the missing passive method used in Gi 's Homogeneity analysis framework.

Published
2012

44. The mandatory adoption of IFRS on intangibles: upheaval or inertia? The case of France

Author

Marie Chavent, Vanessa Kuentz, Elisabeth Walliser, Corinne Bessieux Ollier, GSCM MONTPELLIER FRA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université de Bordeaux (UB), Aménités et dynamiques des espaces ruraux (UR ADBX), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université de Montpellier (UM), Montpellier Research in Management (MRM), Université Montpellier 1 (UM1)-Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School-Université Paul-Valéry - Montpellier 3 (UPVM)-Université de Montpellier (UM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Perpignan Via Domitia (UPVD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université de Perpignan Via Domitia (UPVD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université Paul-Valéry - Montpellier 3 (UPVM)-Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School-Université Montpellier 1 (UM1), Centre d'Etudes et de Recherche sur les Organisations et le Management (CEROM), and Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School

Subjects
Organizational Behavior and Human Resource Management, 050208 finance, business.industry, media_common.quotation_subject, mandatory adoption of IFRS, 05 social sciences, FRANCE, Accounting, 050201 accounting, Inertia, IFRS, Phenomenon, CLUSTER ANALYSIS, 0502 economics and business, [SDE]Environmental Sciences, Economics, [SHS.GESTION]Humanities and Social Sciences/Business administration, business, Finance, ComputingMilieux_MISCELLANEOUS, media_common
Abstract

International audience; This paper examines the effects of mandatory adoption of IFRS on intangibles in the French environment where firms were unable to develop "experience of international standards" before they became mandatory from January 1, 2005. The question we try to answer is the following: will the change of accounting standards concerning the intangibles lead to the upheaval announced in the French accounts or will it rather introduce a phenomenon of inertia on behalf of the firms trying to modify their financial statements at a minimal level? An innovative divisive hierarchical clustering method for firms was applied: the DIV method. The results indicate three clusters of firms, each affected differently by the transition. Only one cluster displays a significant change whereas the others are unaffected by the transition. The inertia phenomenon described by Nobes (2006), arguing that pre-IFRS accounting treatments could survive under IFRS, is thus confirmed.

Published
2012

45. Orthogonal rotation in PCAMIX

Author

Jérôme Saracco, Marie Chavent, K. Vanessa, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
FOS: Computer and information sciences, Statistics and Probability, Angle of rotation, MIXTURE OF QUALITATIVE AND QUANTITATIVE DATA MULTIPLE CORRESPONDENCE ANALYSIS, Applied Mathematics, Varimax rotation, 05 social sciences, Principal (computer security), 050401 social sciences methods, Context (language use), 01 natural sciences, Statistics - Computation, Computer Science Applications, 010104 statistics & probability, 0504 sociology, Multiple correspondence analysis, Singular value decomposition, Principal component analysis, Applied mathematics, 0101 mathematics, [STAT.CO]Statistics [stat]/Computation [stat.CO], Rotation (mathematics), Computation (stat.CO), Mathematics
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM3-ECOPRA; International audience; Kiers (1991) considered the orthogonal rotation in PCAMIX, a principal component method for a mixture of qualitative and quantitative variables. PCAMIX includes the ordinary principal component analysis (PCA) and multiple correspondence analysis (MCA) as special cases. In this paper, we give a new presentation of PCAMIX where the principal components and the squared loadings are obtained from a Singular Value Decomposition. The loadings of the quantitative variables and the principal coordinates of the categories of the qualitative variables are also obtained directly. In this context, we propose a computationaly efficient procedure for varimax rotation in PCAMIX and a direct solution for the optimal angle of rotation. A simulation study shows the good computational behavior of the proposed algorithm. An application on a real data set illustrates the interest of using rotation in MCA. All source codes are available in the R package "PCAmixdata".

Published
2011

46. ClustOfVar : un package R pour la classification de variables

Author

Marie Chavent, Benoit Liquet, Vanessa Kuentz-Simonet, Jérôme Saracco, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Irstea Publications, Migration, INRIA Futurs, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, FOS: Computer and information sciences, [SDE] Environmental Sciences, Fuzzy clustering, dimension reduction, media_common.quotation_subject, Correlation clustering, k-means clustering of variables, computer.software_genre, Statistics - Computation, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, ALGORITHME, Statistics, 0101 mathematics, Cluster analysis, lcsh:Statistics, lcsh:HA1-4737, k-medians clustering, Computation (stat.CO), 030304 developmental biology, Mathematics, Factor analysis, media_common, ANALYSE STATISTIQUE, 0303 health sciences, Variables, stability, Hierarchical clustering, Principal component analysis, [SDE]Environmental Sciences, Data mining, Statistics, Probability and Uncertainty, hierarchical clustering of variables, mixture of quantitative and qualitative variables, computer, Software, METHODOLOGIE
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM [Axe_IRSTEA]DTAM1-REPRO [Axe_IRSTEA]DTAM-QT2-ADAPTATION; Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing as "good as possible" the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two clustering algorithms are proposed to optimize the homogeneity criterion: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets.

Published
2011

47. An adaptive SIR method for block-wise evolving data streams

Author

Marie Chavent, Stéphane Girard, Vanessa Kuentz, Benoit Liquet, Thi Mong Ngoc Nguyen, Jérôme Saracco, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Guglielmo D'Amico and Giuseppe Di Biase and Jacques Janssen and Raimondo Manca, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Irstea, Import Ws

Subjects
numerical simulation, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], regression, numerical model
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM; International audience; In this communication, we consider block-wise evolving data streams. When a semiparametric regression model involving a common dimension reduction direction B is assumed for each block, we propose an adaptive SIR (for sliced inverse regression) estimator of B. This estimator is faster than usual SIR applied to the union of all the blocks, both from computational complexity and running time points of view. We show the consistency of our estimator at the root-n rate. In a simulation, we illustrate the good numerical behaviour of the estimator. We also provide a graphical tool in order to detect if there exists a drift of the dimension reduction direction or some aberrant blocks of data. We illustrate our approach with various scenarios. Finally, possible extensions of this method are given.

Published
2011

48. Modelling scenarios of agriculture changes on freshwater uses and water quality at a large watershed scale: the case of the Charente watershed (France)

Author

Françoise Vernier, Bordenave, P., Marie Chavent, Odile Phelpin_Leccia, Kévin Petit, Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Institut de Mathématiques de Bordeaux (IMB), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)

Subjects
BASSIN VERSANT, PRATIQUE AGRICOLE, POLLUTION D'ORIGINE AGRICOLE, CHARENTE BASSIN VERSANT, [SDE]Environmental Sciences, QUALITE DE L'EAU, RESSOURCES EN EAU, MODELISATION
Abstract

International audience; The Charente River Basin (10 000 km2) is linked to the Pertuis Charentais Sea by a large tidal influence. The agriculture impacts strongly the quantity and the quality of freshwaters and influences the salinity and the quality of coastal waters needed by the oyster culture. In the context of 2 projects dealing with ICZM (EU Spicosa project, Respireau), we focused on the environmental assessment of spatialised scenarios of changes in agriculture. Different methods of clustering and data analysis were used to define a relevant typology of spatial units on the area. Combined with an analysis of agricultural practices based on surveys (irrigation, fertilizers and pesticides), the agricultural activities can be described in a simplified way, to be linked with an Extend model regarding the water quantity and an agro-hydrological one for the water quality side. Scenarios are built using surveys, expertise and discussion with stakeholders. Measures for agriculture changes are either considered in the agro-environmental programs or defined by stakeholders. Their effectiveness on the reduction of nutrients and pesticides fluxes is assessed when applied on the whole area or within sub-basins, depending on the farming system and vulnerability. Assessment was carried out using the spatially distributed model SWAT. The effectiveness of each scenario is considered in terms of relative reduction of nitrates, phosphorus, pesticides, suspended matter compared to the current situation.

Published
2010

49. Clustering of categorical variables around latent variables

Author

Jérome SARACCO (GREThA UMR CNRS 5113), Marie CHAVENT (IMB UMR CNRS 5251), and Vanessa KUENTZ (IMB UMR CNRS 5251)

Subjects
clustering of categorical variables, correlation ratio, iterative relocation algorithm, hierarchical clustering, jel:C49, jel:C69
Abstract

In the framework of clustering, the usual aim is to cluster observations and not variables. However the issue of variable clustering clearly appears for dimension reduction, selection of variables or in some case studies (sensory analysis, biochemistry, marketing, etc.). Clustering of variables is then studied as a way to arrange variables into homogeneous clusters, thereby organizing data into meaningful structures. Once the variables are clustered into groups such that variables are similar to the other variables belonging to their cluster, the selection of a subset of variables is possible. Several specific methods have been developed for the clustering of numerical variables. However concerning categorical variables, much less methods have been proposed. In this paper we extend the criterion used by Vigneau and Qannari (2003) in their Clustering around Latent Variables approach for numerical variables to the case of categorical data. The homogeneity criterion of a cluster of categorical variables is defined as the sum of the correlation ratio between the categorical variables and a latent variable, which is in this case a numerical variable. We show that the latent variable maximizing the homogeneity of a cluster can be obtained with Multiple Correspondence Analysis. Different algorithms for the clustering of categorical variables are proposed: iterative relocation algorithm, ascendant and divisive hierarchical clustering. The proposed methodology is illustrated by a real data application to satisfaction of pleasure craft operators.

Published
2010

50. A Partitioning Method for the Clustering of Categorical Variables

Author

Vanessa Kuentz, Jérôme Saracco, and Marie Chavent

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Fuzzy clustering, Computer science, Multiple correspondence analysis, Dimensionality reduction, Correlation clustering, Latent variable, Data mining, Cluster analysis, Correlation ratio, computer.software_genre, computer, Categorical variable
Abstract

In the framework of clustering, the usual aim is to cluster observations and not variables. However the issue of clustering variables clearly appears for dimension reduction, selection of variables or in some case studies. A simple approach for the clustering of variables could be to construct a dissimilarity matrix between the variables and to apply classical clustering methods. But specific methods have been developed for the clustering of variables. In this context center-based clustering algorithms have been proposed for the clustering of quantitative variables. In this article we extend this approach to categorical variables. The homogeneity criterion of a cluster of categorical variables is based on correlation ratios and Multiple Correspondence Analysis is used to determine the latent variable of each cluster. A simulation study shows that the method recovers well the underlying simulated clusters of variables. Finally an application on a real data set also highlights the practical benefits of the proposed approach.

Published
2010

Catalog

Books, media, physical & digital resources
See catalog results