10,158 results
Search Results
2. Difference in learning among students doing penand-paper homework compared to web-based homework in an introductory statistics course
- Author
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Gunnar Stefansson, Audbjorg Bjornsdottir, Anna Helga Jonsdottir, Raunvísindadeild (HÍ), Faculty of Physical Sciences (UI), Kennslumiðstöð (HA), Centre of Teaching and Learning (UA), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskólinn á Akureyri, University of Akureyri, Háskóli Íslands, and University of Iceland
- Subjects
Statistics and Probability ,Pen-and-paper homework (PPH) ,Web-based homework (WBH) ,Repeated crossover experiments ,01 natural sciences ,Education ,Learning environments ,010104 statistics & probability ,Netið ,Heimanám ,ComputingMilieux_COMPUTERSANDEDUCATION ,Mathematics education ,Web application ,0101 mathematics ,Grading (education) ,Statistics education ,lcsh:LC8-6691 ,Tölfræði ,lcsh:Special aspects of education ,business.industry ,05 social sciences ,050301 education ,Pen and paper homework (PPH) ,Kennsluaðferðir ,lcsh:Probabilities. Mathematical statistics ,Statistics, Probability and Uncertainty ,lcsh:QA273-280 ,business ,Psychology ,0503 education - Abstract
A repeated crossover experiment comparing learning among students handing in pen-and-paper homework (PPH) with students handing in web-based homework (WBH) has been conducted. The system used in the experiments, the tutor-web, has been used to deliver homework problems to thousands of students in mathematics and statistics over several years. Since 2011 experimental changes have been made regarding how the system allocates items to students, how grading is done and the type of feedback provided. The experiment described here was conducted annually from 2011 to 2014. Approximately 100 students in an introductory statistics course participated each year. The main goals were to determine whether the above mentioned changes had an impact on learning as measured by test scores in addition to comparing learning among students doing PPH with students handing in WBH. The difference in learning between students doing WBH compared to PPH, measured by test scores, increased significantly from 2011 to 2014 with an effect size of 0.634. This is a strong indication that the changes made in the tutor-web have a positive impact on learning. Using the data from 2014 a significant difference in learning between WBH and PPH for 2014 was detected with an effect size of 0.416 supporting the use of WBH as a learning tool.
- Published
- 2017
3. What Have We (Not) Learnt from Millions of Scientific Papers with P Values?
- Author
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John P. A. Ioannidis
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Statistics and Probability ,Empirical data ,General Mathematics ,05 social sciences ,01 natural sciences ,050105 experimental psychology ,010104 statistics & probability ,Statistical significance ,Statistics ,Statistical inference ,0501 psychology and cognitive sciences ,p-value ,0101 mathematics ,Statistics, Probability and Uncertainty ,Mathematics ,Statistical hypothesis testing - Abstract
P values linked to null hypothesis significance testing (NHST) is the most widely (mis)used method of statistical inference. Empirical data suggest that across the biomedical literature (1990–2015)...
- Published
- 2019
4. Discussion of the Paper 'Prediction, Estimation, and Attribution' by B. Efron
- Author
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Emmanuel J. Candès and Chiara Sabatti
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Statistics and Probability ,Estimation ,media_common.quotation_subject ,05 social sciences ,01 natural sciences ,010104 statistics & probability ,Core (game theory) ,Reading (process) ,0502 economics and business ,Mathematics education ,Active listening ,0101 mathematics ,Statistics, Probability and Uncertainty ,Attribution ,Psychology ,050205 econometrics ,media_common - Abstract
We enjoyed reading Professor Efron’s (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. One of the core values underlying statistical research is in how it enable...
- Published
- 2020
5. A Look at Robustness and Stability of $\ell_{1}$-versus $\ell_{0}$-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al
- Author
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Peter Bühlmann, Armeen Taeb, and Yuansi Chen
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Statistics and Probability ,latent variables ,low-rank estimation ,General Mathematics ,Linear model ,020206 networking & telecommunications ,Feature selection ,02 engineering and technology ,Latent variable ,01 natural sciences ,Regularization (mathematics) ,010104 statistics & probability ,0202 electrical engineering, electronic engineering, information engineering ,Applied mathematics ,Distributional robustness ,0101 mathematics ,Statistics, Probability and Uncertainty ,high-dimensional estimation ,Mathematics ,variable selection - Abstract
We congratulate the authors Bertsimas, Pauphilet and van Parys (hereafter BPvP) and Hastie, Tibshirani and Tibshirani (hereafter HTT) for providing fresh and insightful views on the problem of variable selection and prediction in linear models. Their contributions at the fundamental level provide guidance for more complex models and procedures.
- Published
- 2020
- Full Text
- View/download PDF
6. Discussion of paper 'nonparametric Bayesian inference in applications' by Peter Müller, Fernando A. Quintana and Garritt L. Page
- Author
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Athanasios Kottas
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0301 basic medicine ,Statistics and Probability ,010104 statistics & probability ,03 medical and health sciences ,030104 developmental biology ,Econometrics ,0101 mathematics ,Statistics, Probability and Uncertainty ,Nonparametric bayesian inference ,01 natural sciences ,Mathematical economics ,Bayesian nonparametrics ,Mathematics - Abstract
This is an invited discussion of review paper “Nonparametric Bayesian Inference in Applications” by Peter Muller, Fernando A. Quintana and Garritt L. Page.
- Published
- 2017
7. From coin tossing to rock-paper-scissors and beyond: a log-exp gap theorem for selecting a leader
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Hsien-Kuei Hwang, Yoshiaki Itoh, and Michael Fuchs
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Statistics and Probability ,Discrete mathematics ,Class (set theory) ,Coin flipping ,Group (mathematics) ,General Mathematics ,Variance (accounting) ,01 natural sciences ,Exponential function ,010104 statistics & probability ,Logarithmic mean ,Bounded function ,0103 physical sciences ,Gap theorem ,0101 mathematics ,Statistics, Probability and Uncertainty ,010306 general physics ,Mathematics - Abstract
A class of games for finding a leader among a group of candidates is studied in detail. This class covers games based on coin tossing and rock-paper-scissors as special cases and its complexity exhibits similar stochastic behaviors: either of logarithmic mean and bounded variance or of exponential mean and exponential variance. Many applications are also discussed.
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- 2017
8. Discussion on the paper by Peter Müller, Fernando A. Quintana, and Garritt L. Page
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Seongil Jo and Jaeyong Lee
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0301 basic medicine ,Statistics and Probability ,Computer science ,Sampling (statistics) ,computer.software_genre ,Mixture model ,01 natural sciences ,Bayesian nonparametrics ,010104 statistics & probability ,03 medical and health sciences ,030104 developmental biology ,Statistics ,Spatial clustering ,Data mining ,0101 mathematics ,Statistics, Probability and Uncertainty ,Variety (universal algebra) ,computer ,Spatial analysis - Abstract
The article by Muller, Quintana, and Page reviews a variety of Bayesian nonparametric models and demonstrates them in a few applications. They emphasize applications in spatial data on which our discussion focuses as well. In particular, we consider two types of mixture models based on species sampling models (SSM) for spatial clustering and apply them to the Chilean mathematics testing score data analyzed by the authors. We conclude that only the mixture model of SSM with spatial locations as part of observations renders spatially non-overlapping clusters.
- Published
- 2017
9. Discussion of the paper ‘A general framework for functional regression modelling’
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Jiawei Bai, Andrada E. Ivanescu, and Ciprian M. Crainiceanu
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Statistics and Probability ,Clustering high-dimensional data ,business.industry ,030229 sport sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Econometrics ,Functional regression ,Artificial intelligence ,0101 mathematics ,Statistics, Probability and Uncertainty ,business ,computer ,Mathematics - Abstract
This discussion provides our reaction to the article by Greven and Scheipl. It contains an overview of their article and a description of the many areas of research that remain open and could benefit from further methodological and computational development.
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- 2017
10. Remarks on a paper of Ahmad, Ahmad and Ahmed
- Author
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Gholamhossein G. Hamedani
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Statistics and Probability ,Pure mathematics ,021103 operations research ,Distribution (number theory) ,lcsh:Mathematics ,0211 other engineering and technologies ,02 engineering and technology ,Management Science and Operations Research ,lcsh:QA1-939 ,01 natural sciences ,statistics , probability ,010104 statistics & probability ,Kumaraswamy distribution ,Modeling and Simulation ,Calculus ,0101 mathematics ,Statistics, Probability and Uncertainty ,60E05, 60E15 ,transmuted distribution, Kumaraswamy distribution, characterizations ,lcsh:Statistics ,lcsh:HA1-4737 ,Mathematics - Abstract
Ahmad et al. (2015) consider a Transmuted Kumaraswamy distribution and study certain properties of their distribution. In the title of their paper they mention characterization of this distribution, but no characterization are presented in their paper. In the present short note, we establish certain characterizations of the Transmuted Kumaraswamy distribution in three directions.
- Published
- 2016
11. Further comments on the paper 'Setting a bonus–malus scale in the presence of other rating factors' by Taylor
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Sojung Carol Park, Kili C. Wang, and Jean Lemaire
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Statistics and Probability ,Economics and Econometrics ,050208 finance ,Scale (ratio) ,business.industry ,Mathematical finance ,05 social sciences ,01 natural sciences ,010104 statistics & probability ,0502 economics and business ,Economics ,Bonus-malus ,Econometrics ,0101 mathematics ,Statistics, Probability and Uncertainty ,business ,Financial services - Published
- 2016
12. Discussion of the synthetic data papers published in the previous issue1
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Jörg Drechsler
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Economics and Econometrics ,Government ,business.industry ,media_common.quotation_subject ,05 social sciences ,Internet privacy ,01 natural sciences ,Pledge ,Management Information Systems ,010104 statistics & probability ,Information sensitivity ,Data access ,Moral obligation ,Data quality ,0502 economics and business ,Confidentiality ,Quality (business) ,Business ,0101 mathematics ,Statistics, Probability and Uncertainty ,050205 econometrics ,media_common - Abstract
In our data driven society in which we expect that all major decisions are backed up by empirical evidence based on high quality data, broad access to these data is a must. However, the benefits of broad data access need to be balanced against potential risks of disclosure. Most data gathered by government agencies are collected under the pledge of confidentiality and the agencies have a legal and moral obligation to guarantee this pledge. Furthermore, if respondents get the impression that their data are not sufficiently protected they might refuse to participate or purposely provide wrong answers jeopardizing the quality of the collected data. Statistical agencies thus have to address this trade-off and much progress has been made in the last decades increasing the amount of data available for the general public while maintaining the confidentiality of the survey respondents. Still, there are certain types of data for which addressing this trade-off is particularly difficult. Medical records containing sensitive information on health status are one example, another example are business data. These data are particularly difficult to protect since a few variables usually suffice to identify larger businesses in the data. At the same time the collected information is often sensitive since other establishments might gain an edge if they learn certain attributes
- Published
- 2016
13. Comment on the paper 'The impact of covariates on a bonus–malus system: an application of Taylor’s model' by Lemaire, Park & Wang
- Author
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Greg Taylor
- Subjects
Statistics and Probability ,Economics and Econometrics ,050208 finance ,business.industry ,Mathematical finance ,05 social sciences ,Individual risk ,01 natural sciences ,010104 statistics & probability ,Risk groups ,0502 economics and business ,Covariate ,Econometrics ,Economics ,Bonus-malus ,0101 mathematics ,Statistics, Probability and Uncertainty ,business ,Financial services - Published
- 2016
14. Preface to the papers on ‘Small area estimation’
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Danny Pfeffermann, Partha Lahiri, Nikos Tzavidis, and Li-Chun Zhang
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Statistics and Probability ,010104 statistics & probability ,Economics and Econometrics ,Small area estimation ,Computer science ,0502 economics and business ,05 social sciences ,0101 mathematics ,Statistics, Probability and Uncertainty ,01 natural sciences ,Social Sciences (miscellaneous) ,050205 econometrics ,Remote sensing - Published
- 2017
15. Discussion of the Paper 'Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models' by Søren Johansen and Bent Nielsen
- Author
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Silvelyn Zwanzig
- Subjects
Statistics and Probability ,Asymptotic analysis ,Series (mathematics) ,05 social sciences ,Bent molecular geometry ,Regression analysis ,01 natural sciences ,010104 statistics & probability ,0502 economics and business ,Anomaly detection ,0101 mathematics ,Statistics, Probability and Uncertainty ,Time complexity ,Algorithm ,050205 econometrics ,Mathematics - Abstract
Discussion of the Paper "Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models" by Soren Johansen and Bent Nielsen
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- 2016
16. Note on A. Barbour’s paper on Stein’s method for diffusion approximations
- Author
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Andrew B. Duncan, Mikołaj Kasprzak, and Sebastian J. Vollmer
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Statistics and Probability ,Pure mathematics ,60B10 ,Statistics & Probability ,Gaussian ,Banach space ,Stein’s method ,01 natural sciences ,010104 statistics & probability ,symbols.namesake ,FOS: Mathematics ,60J65 ,60E05 ,0101 mathematics ,QA ,60J60 ,Mathematics ,Semigroup ,0104 Statistics ,010102 general mathematics ,Probability (math.PR) ,Stein's method ,Heavy traffic approximation ,60F17 ,Primary: 60B10, 60F17, Secondary: 60J60, 60J65, 60E05 ,Norm (mathematics) ,symbols ,Stein equation ,Mathematics [G03] [Physical, chemical, mathematical & earth Sciences] ,Mathématiques [G03] [Physique, chimie, mathématiques & sciences de la terre] ,Statistics, Probability and Uncertainty ,Donsker’s theorem ,Mathematics - Probability ,diffusion approximations - Abstract
In (Barbour, 1990) foundations for diffusion approximation via Stein's method are laid. This paper has been cited more than 130 times and is a cornerstone in the area of Stein's method. A semigroup argument is used therein to solve a Stein equation for Gaussian diffusion approximation. We prove that, contrary to the claim in (Barbour, 1990), the semigroup considered therein is not strongly continuous on the Banach space of continuous, real-valued functions on D[0,1] growing slower than a cubic, equipped with an appropriate norm. We also provide a proof of the exact formulation of the solution to the Stein equation of interest, which does not require the aforementioned strong continuity. This shows that the main results of (Barbour, 1990) hold true., Comment: 8 pages
- Published
- 2017
17. Special Issue with papers from the '3rd workshop on Goodness-of-fit and change-point problems'
- Author
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Claudia Kirch, Simos G. Meintanis, Norbert Henze, and 21262977 - Meintanis, Simos George
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Statistics and Probability ,010104 statistics & probability ,Goodness of fit ,0502 economics and business ,05 social sciences ,Point (geometry) ,0101 mathematics ,Statistics, Probability and Uncertainty ,01 natural sciences ,Mathematical economics ,050205 econometrics ,Mathematics - Abstract
This special issue of METRIKA contains mainly a collection of papers presented at the 3rd Workshop on Goodness-of-Fit and Change-Point problems, which was supported by the German Research Foundation (DFG), and was held at the Church House of the Protestant Academy Baden in Bad Herrenalb in the Black Forest near Karlsruhe, Germany, 8–10 September, 2017. The first workshop in this series took place in 2012 at the University of Sevilla, and the second was held at the National and Kapodistrian University of Athens, 2015. There is already a follow-up event that will take place in 2019 at the University of Trento, Italy. We firmly believe that this series of workshops will become a regular international meeting
- Published
- 2018
18. Comments on the paper ‘Type I error and test power of different tests for testing interaction effects in factorial experiments’ by M. Mendes and S. Yigit (Statistica Neerlandica , 2013, pp. 1-26)
- Author
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Edgar Brunner and Madan L. Puri
- Subjects
Statistics and Probability ,05 social sciences ,Rank (computer programming) ,050401 social sciences methods ,Factorial experiment ,Interaction ,01 natural sciences ,010104 statistics & probability ,0504 sociology ,Sample size determination ,Test power ,Resampling ,Statistics ,0101 mathematics ,Statistics, Probability and Uncertainty ,Mathematics ,Type I and type II errors - Abstract
We give some comments on the paper by Mendes and Yigit where some misleading statements on rank tests have been given. Also, we give some additional important references on the same topic, which are not cited in this paper. By extending the simulations presented in the Mendes and Yigit paper to larger and unequal sample sizes, we demonstrate that the main conclusions are misleading.
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- 2013
19. Corrigendum to the paper: Existence and stability results for semilinear systems of impulsive stochastic differential equations with fractional Brownian motion. Stoch. Anal. Appl. 34 (2016), no. 5, 792–834
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T. Blouhi, Tomás Caraballo, and Abdelghani Ouahab
- Subjects
Statistics and Probability ,Work (thermodynamics) ,Fractional Brownian motion ,Semilinear systems ,Applied Mathematics ,010102 general mathematics ,Stochastic difference equations ,Mathematical analysis ,Stability result ,Fixed point ,01 natural sciences ,010104 statistics & probability ,Stochastic differential equation ,0101 mathematics ,Statistics, Probability and Uncertainty ,Mathematics - Abstract
In this paper we correct an error made in our paper [Blouhi, T.; Caraballo, T.; Ouahab, A. Existence and stability results for semilinear systems of impulsive stochastic differential equations with fractional Brownian motion. Stoch. Anal. Appl. 34 (2016), no. 5, 792-834]. In fact, in this corrigendum we present the correct hypotheses and results, and highlight that the results can be proved using the same method used in the original work. The main feature is that we used a result which has been proved only when the diffusion term does not depend on the unknown.
- Published
- 2017
20. A Note on a Paper by Wong and Heyde
- Author
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Mikhail Urusov and Aleksandar Mijatović
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Statistics and Probability ,Statistics::Theory ,Pure mathematics ,60G44, 60G48, 60H10, 60J60 ,General Mathematics ,Applied probability ,01 natural sciences ,FOS: Economics and business ,010104 statistics & probability ,60G48 ,FOS: Mathematics ,60G44 ,0101 mathematics ,60J60 ,Mathematics ,Local martingales versus true martingales ,010102 general mathematics ,Probability (math.PR) ,stochastic exponential ,Exponential function ,Mathematik ,60H10 ,Statistics, Probability and Uncertainty ,Martingale (probability theory) ,Quantitative Finance - General Finance ,General Finance (q-fin.GN) ,Mathematics - Probability ,Counterexample - Abstract
In this note we re-examine the analysis of the paper "On the martingale property of stochastic exponentials" by B. Wong and C.C. Heyde, Journal of Applied Probability, 41(3):654-664, 2004. Some counterexamples are presented and alternative formulations are discussed., Comment: To appear in Journal of Applied Probability, 11 pages
- Published
- 2011
21. 50-Year Anniversary of Papers by Cormack, Jolly and Seber
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Byron J. T. Morgan and Stephen T. Buckland
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0106 biological sciences ,Statistics and Probability ,010104 statistics & probability ,Computer science ,General Mathematics ,0101 mathematics ,Statistics, Probability and Uncertainty ,010603 evolutionary biology ,01 natural sciences - Published
- 2016
22. Contribution to the discussion of the paper by Stefan Wellek: 'A critical evaluation of the current p -value controversy'
- Author
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Alessio Farcomeni
- Subjects
Statistics and Probability ,Research design ,MEDLINE ,General Medicine ,01 natural sciences ,Type III error ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Econometrics ,P-hacking ,030212 general & internal medicine ,p-value ,0101 mathematics ,Statistics, Probability and Uncertainty ,Current (fluid) ,Mathematical economics ,Mathematics - Published
- 2017
23. Sensitivity of principal component subspaces: A comment on Prendergast's paper
- Author
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Jacques Bénasséni, 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 ), Institut de Recherche Mathématique de Rennes (IRMAR), 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), 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), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)
- Subjects
Statistics and Probability ,RV coefficient ,Mathematical optimization ,Similarity (geometry) ,01 natural sciences ,Measure (mathematics) ,010104 statistics & probability ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,sensitivity measures ,0502 economics and business ,subspaces ,Order (group theory) ,Point (geometry) ,Sensitivity (control systems) ,[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST] ,0101 mathematics ,RV-coefficient ,050205 econometrics ,Mathematics ,62F35, 62H12 ,Principal components ,05 social sciences ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,Linear subspace ,[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH] ,Algebra ,Principal component analysis ,62H12 ,Statistics, Probability and Uncertainty ,62F35 - Abstract
International audience; In a recent paper on sensitivity of subspaces spanned by principal components, [5] introduces an influence measure based on second order expansion of the RV and GCD coefficients which are commonly used as measures of similarity between two matrices. The goal of this short note is to point out that the paper of [2] is based on a similar approach. However this work seems unknown to Prendergast since it is missing in his references. A comparison of the two papers is provided together with a brief review of some related works.
- Published
- 2014
24. Four Papers on Contemporary Software Design Strategies for Statistical Methodologists
- Author
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Dianne Cook and Vincent J. Carey
- Subjects
FOS: Computer and information sciences ,Statistics and Probability ,0303 health sciences ,Engineering ,business.industry ,General Mathematics ,01 natural sciences ,Data science ,Methodology (stat.ME) ,010104 statistics & probability ,03 medical and health sciences ,Software design ,Statistical analysis ,0101 mathematics ,Statistics, Probability and Uncertainty ,business ,Statistical software ,Statistics - Methodology ,030304 developmental biology - Abstract
Software design impacts much of statistical analysis and, as technology changes, dramatically so in recent years, it is exciting to learn how statistical software is adapting and changing. This leads to the collection of papers published here, written by John Chambers, Duncan Temple Lang, Michael Lawrence, Martin Morgan, Yihui Xie, Heike Hofmann and Xiaoyue Cheng., Published in at http://dx.doi.org/10.1214/14-STS481 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
- Published
- 2014
25. Comments on the paper 'Helping reviewers ask the right questions: The InfoQ framework for reviewing applied research' by Ron S. Kenett and Galit Shmueli
- Author
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Asta Manninen
- Subjects
Economics and Econometrics ,030505 public health ,01 natural sciences ,Data science ,Management Information Systems ,010104 statistics & probability ,03 medical and health sciences ,Ask price ,Applied research ,Engineering ethics ,Sociology ,0101 mathematics ,Statistics, Probability and Uncertainty ,0305 other medical science - Published
- 2016
26. Comments on the paper 'Helping reviewers ask the right questions: The InfoQ framework for reviewing applied research' by Ron S. Kenett and Galit Shmueli
- Author
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Gemma Van Halderen
- Subjects
Economics and Econometrics ,030505 public health ,01 natural sciences ,Data science ,Management Information Systems ,010104 statistics & probability ,03 medical and health sciences ,Ask price ,Applied research ,Engineering ethics ,Sociology ,0101 mathematics ,Statistics, Probability and Uncertainty ,0305 other medical science - Published
- 2016
27. Comments on the paper 'Helping reviewers ask the right questions: The InfoQ framework for reviewing applied research' by Ron S. Kenett and Galit Shmueli
- Author
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M.A. Hidiroglou
- Subjects
Economics and Econometrics ,030505 public health ,01 natural sciences ,Data science ,Management Information Systems ,010104 statistics & probability ,03 medical and health sciences ,Ask price ,Engineering ethics ,Applied research ,Sociology ,0101 mathematics ,Statistics, Probability and Uncertainty ,0305 other medical science - Published
- 2016
28. Comments on the paper 'Helping reviewers ask the right questions: The InfoQ framework for reviewing applied research' by Ron S. Kenett and Galit Shmueli
- Author
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Michele Connolly
- Subjects
Economics and Econometrics ,030505 public health ,01 natural sciences ,Data science ,Management Information Systems ,010104 statistics & probability ,03 medical and health sciences ,Ask price ,Applied research ,Engineering ethics ,Sociology ,0101 mathematics ,Statistics, Probability and Uncertainty ,0305 other medical science - Published
- 2016
29. Editorial for the special issue on ‘Papers inspired by the Workshop 'Nonparametric Statistics: Refined, Redefined, and Renewed'’
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Madan L. Puri, Robert Serfling, and Edgar Brunner
- Subjects
Statistics and Probability ,010104 statistics & probability ,0502 economics and business ,05 social sciences ,Econometrics ,Nonparametric statistics ,0101 mathematics ,Statistics, Probability and Uncertainty ,01 natural sciences ,Data science ,050205 econometrics ,Mathematics - Published
- 2010
30. Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross‐validation: Methodological approach and data‐based evaluation
- Author
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Erika Banzato, Liesbeth C. de Wreede, and Bart Mertens
- Subjects
cross‐validation ,Statistics and Probability ,Biometry ,multiple imputation ,Computer science ,Logistic regression ,computer.software_genre ,cross-validation ,01 natural sciences ,binary outcome ,Cross-validation ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Software ,030212 general & internal medicine ,Imputation (statistics) ,0101 mathematics ,Analysis of Variance ,Estimation theory ,business.industry ,Binary outcome ,prediction ,General Medicine ,Missing data ,Research Papers ,Brier score ,Calibration ,Data mining ,Statistics, Probability and Uncertainty ,business ,computer ,Research Paper - Abstract
We investigate calibration and assessment of predictive rules when missing values are present in the predictors. Our paper has two key objectives. The first is to investigate how the calibration of the prediction rule can be combined with use of multiple imputation to account for missing predictor observations. The second objective is to propose such methods that can be implemented with current multiple imputation software, while allowing for unbiased predictive assessment through validation on new observations for which outcome is not yet available. We commence with a review of the methodological foundations of multiple imputation as a model estimation approach as opposed to a purely algorithmic description. We specifically contrast application of multiple imputation for parameter (effect) estimation with predictive calibration. Based on this review, two approaches are formulated, of which the second utilizes application of the classical Rubin's rules for parameter estimation, while the first approach averages probabilities from models fitted on single imputations to directly approximate the predictive density for future observations. We present implementations using current software that allow for validation and estimation of performance measures by cross‐validation, as well as imputation of missing data in predictors on the future data where outcome is missing by definition. To simplify, we restrict discussion to binary outcome and logistic regression throughout. Method performance is verified through application on two real data sets. Accuracy (Brier score) and variance of predicted probabilities are investigated. Results show substantial reductions in variation of calibrated probabilities when using the first approach.
- Published
- 2020
31. Pricing participating longevity-linked life annuities: a Bayesian Model Ensemble approach
- Author
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Bravo, Jorge Miguel
- Subjects
Statistics and Probability ,Economics and Econometrics ,Population ,Asset allocation ,Bayesian Model Ensemble ,01 natural sciences ,Pensions ,010104 statistics & probability ,0502 economics and business ,Economics ,Econometrics ,Longevity-linked life annuities ,C15 ,Longevity options ,Stochastic mortality models ,0101 mathematics ,education ,health care economics and organizations ,Valuation (finance) ,education.field_of_study ,050208 finance ,Longevity risk ,G13 ,Mathematical finance ,05 social sciences ,Life annuity ,Original Research Paper ,G22 ,Risk pool ,G23 ,Statistics, Probability and Uncertainty ,Upside potential ratio - Abstract
Participating longevity-linked life annuities (PLLA) in which benefits are updated periodically based on the observed survival experience of a given underlying population and the performance of the investment portfolio are an alternative insurance product offering consumers individual longevity risk protection and the chance to profit from the upside potential of financial market developments. This paper builds on previous research on the design and pricing of PLLAs by considering a Bayesian Model Ensemble of single population generalised age-period-cohort stochastic mortality models in which individual forecasts are weighted by their posterior model probabilities. For the valuation, we adopt a longevity option decomposition approach with risk-neutral simulation and investigate the sensitivity of results to changes in the asset allocation by considering a more aggressive lifecycle strategy. We calibrate models using Taiwanese (mortality, yield curve and stock market) data from 1980 to 2019. The empirical results provide significant valuation and policy insights for the provision of a cost effective and efficient risk pooling mechanism that addresses the individual uncertainty of death, while providing appropriate retirement income and longevity protection.
- Published
- 2021
32. Globaltest confidence regions and their application to ridge regression
- Author
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Jelle J. Goeman, Aldo Solari, Ningning Xu, Xu, N, Solari, A, and Goeman, J
- Subjects
Statistics and Probability ,confidence regions ,Mean squared error ,high dimensional ,High‐dimensional or Clustered Data ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Statistics ,Point (geometry) ,tuning parameter selection ,Computer Simulation ,030212 general & internal medicine ,0101 mathematics ,Mathematics ,Confidence region ,Proportional Hazards Models ,Linear model ,General Medicine ,confidence region ,Ridge (differential geometry) ,Regression ,Linear Models ,Selection method ,Statistics, Probability and Uncertainty ,Focus (optics) ,Research Paper - Abstract
We construct confidence regions in high dimensions by inverting the globaltest statistics, and use them to choose the tuning parameter for penalized regression. The selected model corresponds to the point in the confidence region of the parameters that minimizes the penalty, making it the least complex model that still has acceptable fit according to the test that defines the confidence region. As the globaltest is particularly powerful in the presence of many weak predictors, it connects well to ridge regression, and we thus focus on ridge penalties in this paper. The confidence region method is quick to calculate, intuitive, and gives decent predictive potential. As a tuning parameter selection method it may even outperform classical methods such as cross‐validation in terms of mean squared error of prediction, especially when the signal is weak. We illustrate the method for linear models in simulation study and for Cox models in real gene expression data of breast cancer samples.
- Published
- 2021
33. Modeling and computation of multistep batch testing for infectious diseases
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Xiaolin Li, Haoran Jiang, and Hongshik Ahn
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Statistics and Probability ,Mathematical optimization ,optimal batch size ,Computer science ,Monte Carlo method ,coronavirus ,sample pooling ,specificity ,Communicable Diseases ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,COVID-19 Testing ,0302 clinical medicine ,Humans ,Computer Simulation ,030212 general & internal medicine ,Sensitivity (control systems) ,0101 mathematics ,Pandemics ,Flexibility (engineering) ,SARS-CoV-2 ,Numerical analysis ,COVID-19 ,Statistical model ,General Medicine ,sensitivity ,Research Papers ,Nonlinear system ,Variable (computer science) ,False positive rate ,Statistics, Probability and Uncertainty ,Research Paper - Abstract
We propose a mathematical model based on probability theory to optimize COVID‐19 testing by a multistep batch testing approach with variable batch sizes. This model and simulation tool dramatically increase the efficiency and efficacy of the tests in a large population at a low cost, particularly when the infection rate is low. The proposed method combines statistical modeling with numerical methods to solve nonlinear equations and obtain optimal batch sizes at each step of tests, with the flexibility to incorporate geographic and demographic information. In theory, this method substantially improves the false positive rate and positive predictive value as well. We also conducted a Monte Carlo simulation to verify this theory. Our simulation results show that our method significantly reduces the false negative rate. More accurate assessment can be made if the dilution effect or other practical factors are taken into consideration. The proposed method will be particularly useful for the early detection of infectious diseases and prevention of future pandemics. The proposed work will have broader impacts on medical testing for contagious diseases in general.
- Published
- 2021
34. Epidemics with carriers: A note on a paper of Dietz
- Author
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F. Downton
- Subjects
Statistics and Probability ,Entire population ,education.field_of_study ,General Mathematics ,010102 general mathematics ,Population ,01 natural sciences ,Short interval ,010104 statistics & probability ,0101 mathematics ,Statistics, Probability and Uncertainty ,education ,Demography ,Mathematics - Abstract
In a recent paper Weiss (1965) has suggested a simple model for a carrier-borne epidemic such as typhoid. He considers a population (of size m) of susceptibles into which a number (k) of carriers is introduced. These carriers exhibit no overt symptoms and are only detectable by the discovery of infected persons. He supposed that after the initial introduction of the carriers, the population remains entirely closed and no new carriers arise. The epidemic then progresses until either all the carriers have been traced and isolated or until the entire population has succumbed to the disease.
- Published
- 1967
35. A comparison of methods for analysing multiple outcome measures in randomised controlled trials using a simulation study
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Rumana Z Omar, Gareth Ambler, and Victoria Vickerstaff
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Statistics and Probability ,Multiple outcome ,Multivariate statistics ,Computer science ,multiple endpoints ,multivariate model ,Intervention effect ,randomised controlled trials ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Bias ,Outcome Assessment, Health Care ,Statistics ,Computer Simulation ,030212 general & internal medicine ,Imputation (statistics) ,0101 mathematics ,Randomized Controlled Trials as Topic ,multiple outcomes ,Univariate ,Outcome measures ,General Medicine ,Missing data ,Research Design ,Data Interpretation, Statistical ,Pairwise comparison ,Statistics, Probability and Uncertainty ,Trial and Survey Methodology ,Research Paper - Abstract
Multiple primary outcomes are sometimes collected and analysed in randomised controlled trials (RCTs), and are used in favour of a single outcome. By collecting multiple primary outcomes, it is possible to fully evaluate the effect that an intervention has for a given disease process. A simple approach to analysing multiple outcomes is to consider each outcome separately, however, this approach does not account for any pairwise correlations between the outcomes. Any cases with missing values must be ignored, unless an additional imputation step is performed. Alternatively, multivariate methods that explicitly model the pairwise correlations between the outcomes may be more efficient when some of the outcomes have missing values. In this paper, we present an overview of relevant methods that can be used to analyse multiple outcome measures in RCTs, including methods based on multivariate multilevel (MM) models. We perform simulation studies to evaluate the bias in the estimates of the intervention effects and the power of detecting true intervention effects observed when using selected methods. Different simulation scenarios were constructed by varying the number of outcomes, the type of outcomes, the degree of correlations between the outcomes and the proportions and mechanisms of missing data. We compare multivariate methods to univariate methods with and without multiple imputation. When there are strong correlations between the outcome measures (ρ > .4), our simulation studies suggest that there are small power gains when using the MM model when compared to analysing the outcome measures separately. In contrast, when there are weak correlations (ρ < .4), the power is reduced when using univariate methods with multiple imputation when compared to analysing the outcome measures separately.
- Published
- 2020
36. On the relation between the cause‐specific hazard and the subdistribution rate for competing risks data: The Fine–Gray model revisited
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Hans C. van Houwelingen, Martin Schumacher, and Hein Putter
- Subjects
Statistics and Probability ,Biometry ,cause-specific hazard ,media_common.quotation_subject ,Inference ,Competing risks ,Risk Assessment ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Covariate ,cause‐specific hazard ,Econometrics ,030212 general & internal medicine ,0101 mathematics ,cumulative incidence ,Reduction factor ,competing risks ,media_common ,Analysis of Variance ,Models, Statistical ,subdistribution hazard ,General Medicine ,proportional hazards ,Research Papers ,Feeling ,Subdistribution hazard ,Statistics, Probability and Uncertainty ,Psychology ,Gray (horse) ,Cause specific hazard ,Research Paper - Abstract
The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine–Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine–Gray and cause‐specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine–Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause‐specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation.
- Published
- 2020
37. Optimal promising zone designs
- Author
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Cyrus R. Mehta, Lingyun Liu, and Samuel T. Hsiao
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Statistics and Probability ,Optimal design ,Mathematical optimization ,Biometry ,Computer science ,01 natural sciences ,gold standard sample size reassessment rule ,promising zone design ,sample size reassessment ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Interim ,Test statistic ,adaptive design ,power comparisons of adaptive versus nonadaptive ,Humans ,030212 general & internal medicine ,group sequential design ,0101 mathematics ,trial optimization ,Expected utility hypothesis ,Clinical Trials as Topic ,optimal adaptive design ,General Medicine ,Decision rule ,Class (biology) ,Research Papers ,Pancreatic Neoplasms ,Sample size determination ,Statistics, Probability and Uncertainty ,Construct (philosophy) ,Research Paper - Abstract
Clinical trials with adaptive sample size reassessment based on an unblinded analysis of interim results are perhaps the most popular class of adaptive designs (see Elsäßer et al., 2007). Such trials are typically designed by prespecifying a zone for the interim test statistic, termed the promising zone, along with a decision rule for increasing the sample size within that zone. Mehta and Pocock (2011) provided some examples of promising zone designs and discussed several procedures for controlling their type‐1 error. They did not, however, address how to choose the promising zone or the corresponding sample size reassessment rule, and proposed instead that the operating characteristics of alternative promising zone designs could be compared by simulation. Jennison and Turnbull (2015) developed an approach based on maximizing expected utility whereby one could evaluate alternative promising zone designs relative to a gold‐standard optimal design. In this paper, we show how, by eliciting a few preferences from the trial sponsor, one can construct promising zone designs that are both intuitive and achieve the Jennison and Turnbull (2015) gold‐standard for optimality.
- Published
- 2018
38. Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data
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Magnus M. Münch, Mark A. van de Wiel, Sylvia Richardson, Gwenaël G. R. Leday, APH - Methodology, and Epidemiology and Data Science
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Statistics and Probability ,Drug ,Multivariate statistics ,Computer science ,media_common.quotation_subject ,Normal Distribution ,Genomics of Drug Sensitivity in Cancer (GDSC) ,01 natural sciences ,Inverse Gaussian distribution ,010104 statistics & probability ,03 medical and health sciences ,symbols.namesake ,Bayes' theorem ,0302 clinical medicine ,Linear regression ,Feature (machine learning) ,030212 general & internal medicine ,Sensitivity (control systems) ,0101 mathematics ,Novel Bayesian Developments in Clinical Trials ,drug sensitivity ,Precision Medicine ,media_common ,Shrinkage ,variational Bayes ,Bayes Theorem ,General Medicine ,Genomics ,Pharmaceutical Preparations ,symbols ,Statistics, Probability and Uncertainty ,Algorithm ,empirical Bayes ,Research Paper - Abstract
In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. These types of problems entail modelling multivariate drug responses on high-dimensional molecular feature sets in typically >1000 cell lines. The dimensions of the problem require specialised models and estimation methods. In addition, external information on both the drugs and the features is often available. We propose to model the drug responses through a linear regression with shrinkage enforced through a normal inverse Gaussian prior. We let the prior depend on the external information, and estimate the model and external information dependence in an empirical-variational Bayes framework. We demonstrate the usefulness of this model in both a simulated setting and in the publicly available Genomics of Drug Sensitivity in Cancer data.
- Published
- 2021
39. Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework
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Mark J. Rutherford, Elisavet Syriopoulou, and Paul C. Lambert
- Subjects
Statistics and Probability ,Inequality ,Colorectal cancer ,natural indirect effect ,media_common.quotation_subject ,Psychological intervention ,01 natural sciences ,Cancer prognosis ,010104 statistics & probability ,03 medical and health sciences ,cancer inequalities ,0302 clinical medicine ,regression standardization ,Epidemiology of cancer ,Medicine ,Humans ,030212 general & internal medicine ,0101 mathematics ,Recent Developments in Survival Analysis ,media_common ,Mediation Analysis ,Relative survival ,business.industry ,Cancer ,relative survival ,General Medicine ,medicine.disease ,Survival Analysis ,Regression ,Colonic Neoplasms ,Statistics, Probability and Uncertainty ,business ,Demography ,Research Paper - Abstract
Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer‐related and other‐cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer‐related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all‐cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer‐related differences instead of all‐cause differences and helps improve our understanding on cancer inequalities.
- Published
- 2020
40. Simulating longitudinal data from marginal structural models using the additive hazard model
- Author
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Jon Michael Gran, Shaun R. Seaman, Ruth H. Keogh, Stijn Vansteelandt, Keogh, Ruth H. [0000-0001-6504-3253], Seaman, Shaun R. [0000-0003-3726-5937], Vansteelandt, Stijn [0000-0002-4207-8733], Apollo - University of Cambridge Repository, Keogh, Ruth H [0000-0001-6504-3253], and Seaman, Shaun R [0000-0003-3726-5937]
- Subjects
marginal structural model ,FOS: Computer and information sciences ,longitudinal data ,Computer science ,Marginal structural model ,additive hazard model ,01 natural sciences ,time-dependent confounding ,survival analysis ,010104 statistics & probability ,0302 clinical medicine ,Software ,RESEARCH PAPERS ,Econometrics ,030212 general & internal medicine ,causal inference ,Statistics ,General Medicine ,Outcome (probability) ,Mathematics and Statistics ,SURVIVAL ,INVERSE PROBABILITY WEIGHTS ,Statistics, Probability and Uncertainty ,RESEARCH PAPER ,Statistics and Probability ,Hazard (logic) ,Article ,Methodology (stat.ME) ,03 medical and health sciences ,Covariate ,Computer Simulation ,0101 mathematics ,Statistics - Methodology ,Proportional Hazards Models ,Models, Statistical ,Proportional hazards model ,business.industry ,COX ,Probability and statistics ,congenial models ,simulation study ,FAILURE TIME MODELS ,Models, Structural ,time‐dependent confounding ,CAUSAL INFERENCE ,Causal inference ,Probability and Uncertainty ,dependent confounding ,time‐ ,business - Abstract
Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time‐dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g‐formula, are popular for handling this problem. With increasing development of advanced causal inference methods, it is important to be able to assess their performance in different scenarios to guide their application. Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited. This paper focuses on the use of simulations for evaluations involving MSMs in studies with a time‐to‐event outcome. In a simulation, it is important to be able to generate the data in such a way that the correct forms of any models to be fitted to those data are known. However, this is not straightforward in the longitudinal setting because it is natural for data to be generated in a sequential conditional manner, whereas MSMs involve fitting marginal rather than conditional hazard models. We provide general results that enable the form of the correctly specified MSM to be derived based on a conditional data generating procedure, and show how the results can be applied when the conditional hazard model is an Aalen additive hazard or Cox model. Using conditional additive hazard models is advantageous because they imply additive MSMs that can be fitted using standard software. We describe and illustrate a simulation algorithm. Our results will help researchers to effectively evaluate causal inference methods via simulation.
- Published
- 2020
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41. Longitudinal analysis of pre‐ and post‐treatment measurements with equal baseline assumptions in randomized trials
- Author
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Takashi Funatogawa and Ikuko Funatogawa
- Subjects
Statistics and Probability ,Biometry ,Bivariate analysis ,01 natural sciences ,law.invention ,repeated measure ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Randomized controlled trial ,law ,Statistics ,pretest ,Humans ,030212 general & internal medicine ,Longitudinal Studies ,0101 mathematics ,Baseline (configuration management) ,Pre and post ,Mathematics ,analysis of covariance ,Randomized Controlled Trials as Topic ,Analysis of covariance ,posttest ,Estimator ,longitudinal analysis ,General Medicine ,Research Papers ,Sample size determination ,unequal variance ,Statistics, Probability and Uncertainty ,Type I and type II errors ,Research Paper - Abstract
For continuous variables of randomized controlled trials, recently, longitudinal analysis of pre‐ and posttreatment measurements as bivariate responses is one of analytical methods to compare two treatment groups. Under random allocation, means and variances of pretreatment measurements are expected to be equal between groups, but covariances and posttreatment variances are not. Under random allocation with unequal covariances and posttreatment variances, we compared asymptotic variances of the treatment effect estimators in three longitudinal models. The data‐generating model has equal baseline means and variances, and unequal covariances and posttreatment variances. The model with equal baseline means and unequal variance–covariance matrices has a redundant parameter. In large sample sizes, these two models keep a nominal type I error rate and have high efficiency. The model with equal baseline means and equal variance–covariance matrices wrongly assumes equal covariances and posttreatment variances. Only under equal sample sizes, this model keeps a nominal type I error rate. This model has the same high efficiency with the data‐generating model under equal sample sizes. In conclusion, longitudinal analysis with equal baseline means performed well in large sample sizes. We also compared asymptotic properties of longitudinal models with those of the analysis of covariance (ANCOVA) and t‐test.
- Published
- 2019
42. Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control
- Author
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Annette Kopp-Schneider, Manuel Wiesenfarth, and Silvia Calderazzo
- Subjects
Statistics and Probability ,Adult ,Biometry ,Computer science ,Transparency (market) ,media_common.quotation_subject ,Control (management) ,Bayesian probability ,01 natural sciences ,Outcome (game theory) ,Conformity ,Pediatrics ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,robust prior ,Humans ,030212 general & internal medicine ,0101 mathematics ,Function (engineering) ,media_common ,Clinical Trials as Topic ,historical information ,Uniformly most powerful test ,evidence synthesis ,General Medicine ,Research Papers ,frequentist error control ,Risk analysis (engineering) ,Research Design ,Bayesian dynamic borrowing of information ,Statistics, Probability and Uncertainty ,Type I and type II errors ,Research Paper - Abstract
In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease‐treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision‐making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.
- Published
- 2019
43. Effect size measures and their benchmark values for quantifying benefit or risk of medicinal products
- Author
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Volker W. Rahlfs and Helmuth Zimmermann
- Subjects
Statistics and Probability ,effect size measures ,Biometry ,Drug-Related Side Effects and Adverse Reactions ,Distribution (number theory) ,transformation of measures ,binary ,Value (computer science) ,Risk Assessment ,01 natural sciences ,Measure (mathematics) ,Normal distribution ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Statistics ,030212 general & internal medicine ,0101 mathematics ,continuous data ,Proportional Hazards Models ,Mathematics ,General Biometry ,Stochastic Processes ,Absolute risk reduction ,Mann–Whitney measure ,ordinal ,General Medicine ,clinical relevance ,Benchmarking ,Strictly standardized mean difference ,Binary data ,Benchmark (computing) ,Statistics, Probability and Uncertainty ,Research Paper - Abstract
The standardized mean difference is a well‐known effect size measure for continuous, normally distributed data. In this paper we present a general basis for important other distribution families. As a general concept, usable for every distribution family, we introduce the relative effect, also called Mann–Whitney effect size measure of stochastic superiority. This measure is a truly robust measure, needing no assumptions about a distribution family. It is thus the preferred tool for assumption‐free, confirmatory studies. For normal distribution shift, proportional odds, and proportional hazards, we show how to derive many global values such as risk difference average, risk difference extremum, and odds ratio extremum. We demonstrate that the well‐known benchmark values of Cohen with respect to group differences—small, medium, large—can be translated easily into corresponding Mann–Whitney values. From these, we get benchmarks for parameters of other distribution families. Furthermore, it is shown that local measures based on binary data (2 × 2 tables) can be associated with the Mann–Whitney measure: The concept of stochastic superiority can always be used. It is a general statistical value in every distribution family. It therefore yields a procedure for standardizing the assessment of effect size measures. We look at the aspect of relevance of an effect size and—introducing confidence intervals—present some examples for use in statistical practice.
- Published
- 2019
44. Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization
- Author
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Gerton Lunter and Donna Henderson
- Subjects
Statistics and Probability ,Mathematical optimization ,Original Paper ,Computer science ,Monte Carlo method ,Parameterized complexity ,Latent variable model ,02 engineering and technology ,Online estimation ,Stochastic approximation ,01 natural sciences ,010104 statistics & probability ,Computational Mathematics ,Expectation–maximization algorithm ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Adaptive learning ,0101 mathematics ,Statistics, Probability and Uncertainty ,Particle filter ,Stochastic approximation expectation maximization ,Sequential Monte Carlo ,Sufficient statistic - Abstract
Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are intractable, stochastic approximation EM (SAEM) is often used, which uses Monte Carlo techniques to approximate the expected log likelihood. Two common implementations of SAEM, Batch EM (BEM) and online EM (OEM), are parameterized by a “learning rate”, and their efficiency depend strongly on this parameter. We propose an extension to the OEM algorithm, termed Introspective Online Expectation Maximization (IOEM), which removes the need for specifying this parameter by adapting the learning rate to trends in the parameter updates. We show that our algorithm matches the efficiency of the optimal BEM and OEM algorithms in multiple models, and that the efficiency of IOEM can exceed that of BEM/OEM methods with optimal learning rates when the model has many parameters. Finally we use IOEM to fit two models to a financial time series. A Python implementation is available at https://github.com/luntergroup/IOEM.git.
- Published
- 2019
45. Direct statistical inference for finite Markov jump processes via the matrix exponential
- Author
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Sherlock, Chris
- Subjects
FOS: Computer and information sciences ,Statistics and Probability ,Bayesian inference ,Population ,01 natural sciences ,Methodology (stat.ME) ,010104 statistics & probability ,03 medical and health sciences ,Matrix (mathematics) ,symbols.namesake ,62M05 ,Statistical inference ,Applied mathematics ,0101 mathematics ,education ,Markov jump process ,Statistics - Methodology ,030304 developmental biology ,Mathematics ,Original Paper ,0303 health sciences ,education.field_of_study ,Markov chain ,Stochastic matrix ,Markov chain Monte Carlo ,Matrix exponential ,Likelihood inference ,Computational Mathematics ,symbols ,Statistics, Probability and Uncertainty - Abstract
Given noisy, partial observations of a time-homogeneous, finite-statespace Markov chain, conceptually simple, direct statistical inference is available, in theory, via its rate matrix, or infinitesimal generator, $\mathsf{Q}$, since $\exp (\mathsf{Q}t)$ is the transition matrix over time $t$. However, perhaps because of inadequate tools for matrix exponentiation in programming languages commonly used amongst statisticians or a belief that the necessary calculations are prohibitively expensive, statistical inference for continuous-time Markov chains with a large but finite state space is typically conducted via particle MCMC or other relatively complex inference schemes. When, as in many applications $\mathsf{Q}$ arises from a reaction network, it is usually sparse. We describe variations on known algorithms which allow fast, robust and accurate evaluation of the product of a non-negative vector with the exponential of a large, sparse rate matrix. Our implementation uses relatively recently developed, efficient, linear algebra tools that take advantage of such sparsity. We demonstrate the straightforward statistical application of the key algorithm on a model for the mixing of two alleles in a population and on the Susceptible-Infectious-Removed epidemic model., Focus much more on statistical inference, filtering and prediction
- Published
- 2021
46. An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions
- Author
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Fabio Divino, Giovanna Jona-Lasinio, Gianfranco Lovison, Alessio Farcomeni, Antonello Maruotti, Farcomeni A., Maruotti A., Divino F., Jona-Lasinio G., and Lovison G.
- Subjects
FOS: Computer and information sciences ,Statistics and Probability ,Time Factors ,Occupancy ,Coronavirus disease 2019 (COVID-19) ,Computer science ,01 natural sciences ,Generalized linear mixed model ,SARS‐CoV‐2 ,law.invention ,clustered data ,COVID-19 ,generalized linear mixed model ,integer autoregressive ,integer autoregressive model ,panel data ,SARS-CoV-2 ,weighted ensemble ,Methodology (stat.ME) ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,law ,COVID‐19 ,Intensive care ,Econometrics ,Humans ,030212 general & internal medicine ,0101 mathematics ,Pandemics ,Statistics - Methodology ,Reproducibility of Results ,General Medicine ,Intensive care unit ,Research Papers ,Term (time) ,Intensive Care Units ,Autoregressive model ,Italy ,Nonlinear Dynamics ,Forecasting ,Statistics, Probability and Uncertainty ,Settore SECS-S/01 ,Settore SECS-S/01 - Statistica ,Panel data ,Research Paper - Abstract
The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.
- Published
- 2020
47. Nowcasting the COVID-19 pandemic in Bavaria
- Author
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Felix Günther, Michael Höhle, Katharina Katz, Helmut Küchenhoff, and Andreas Bender
- Subjects
Statistics and Probability ,Disease onset ,Situation awareness ,Coronavirus disease 2019 (COVID-19) ,Nowcasting ,Computer science ,Health authority ,Psychological intervention ,nowcasting ,Bayesian inference ,01 natural sciences ,Synthetic data ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,COVID‐19 ,Germany ,Pandemic ,Econometrics ,Bayesian hierarchical modeling ,Humans ,030212 general & internal medicine ,0101 mathematics ,Pandemics ,Bayesian hierarchical model ,Retrospective Studies ,Estimation ,Models, Statistical ,infectious disease epidemiology ,COVID-19 ,Bayes Theorem ,General Medicine ,Data science ,Research Papers ,epidemic surveillance ,Statistics, Probability and Uncertainty ,Research Paper - Abstract
To assess the current dynamic of an epidemic it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, e.g., if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamic of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID–19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-dependent case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID–19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current epidemic, and evaluate the model based on synthetic and retrospective data on COVID–19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis is available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaptions of our approach to different data.
- Published
- 2020
48. Anisotropy of Hölder Gaussian random fields: characterization, estimation, and application to image textures
- Author
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Frédéric Richard, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)
- Subjects
Statistics and Probability ,Field (physics) ,Gaussian ,02 engineering and technology ,anisotropy ,01 natural sciences ,Measure (mathematics) ,photographic paper ,Theoretical Computer Science ,Combinatorics ,010104 statistics & probability ,symbols.namesake ,quadratic variations ,Probability theory ,0202 electrical engineering, electronic engineering, information engineering ,Holder regularity ,Statistical physics ,0101 mathematics ,62M40 ,Anisotropy ,Brownian motion ,Mathematics ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Random field ,Isotropy ,fractional Brownian field ,Computational Theory and Mathematics ,Texture analysis ,symbols ,020201 artificial intelligence & image processing ,Statistics, Probability and Uncertainty ,[STAT.ME]Statistics [stat]/Methodology [stat.ME] ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing - Abstract
International audience; The characterization and estimation of the Hölder regularity of random fields has long been an important topic of Probability theory and Statistics. This notion of regularity has also been widely used in Image Analysis to measure the roughness of textures. However, such a measure is often not sufficient to characterize textures as it does account for their directional properties (e.g. isotropy and anisotropy). In this paper, we present an approach to further characterize directional properties associated to the Holder regularity of random fields. Using the spectral density, we define a notion of asymptotic topothesy which quantifies directional contributions of field high-frequencies to the Holder regularity. This notion is related to the topothesy function of the so-called anisotropic fractional Brownian fields, but is defined in a more generic framework of intrinsic random fields. We then propose a method based on multi-oriented quadratic variations to estimate this asymptotic topothesy. Eventually, we evaluate this method on synthetic data and apply it for the characterization of historical photographic papers.
- Published
- 2018
49. Sex-specific mortality forecasting for UK countries: a coherent approach
- Author
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Ree Yongqing Chen, Pietro Millossovich, Chen, Yongqing, and Millossovich, Pietro
- Subjects
Statistics and Probability ,Economics and Econometrics ,Specific time ,Population ,Poisson distribution ,HG ,01 natural sciences ,Lee-Carter ,Divergence ,Mortality projection ,010104 statistics & probability ,symbols.namesake ,Coherent forecast ,0502 economics and business ,Econometrics ,Common factor ,Cohort term ,0101 mathematics ,education ,education.field_of_study ,050208 finance ,Mathematical finance ,05 social sciences ,Specific mortality ,Term (time) ,Original Research Paper ,Geography ,Cohort ,symbols ,Statistics, Probability and Uncertainty - Abstract
This paper introduces a gender specific model for the joint mortality projection of three countries (England and Wales combined, Scotland, and Northern Ireland) of the United Kingdom. The model, called 2-tier Augmented Common Factor model, extends the classical Lee and Carter [26] and Li and Lee [32] models, with a common time factor for the whole UK population, a sex specific period factor for males and females, and a specific time factor for each country within each gender. As death counts in each subpopulation are modelled directly, a Poisson framework is used. Our results show that the 2-tier ACF model improves the in-sample fitting compared to the use of independent LC models for each subpopulation or of independent Li and Lee models for each couple of genders within each country. Mortality projections also show that the 2-tier ACF model produces coherent forecasts for the two genders within each country and different countries within each gender, thus avoiding the divergence issues arising when independent projections are used. The 2-tier ACF is further extended to include a cohort term to take into account the faster improvements of the UK ‘golden generation’.
- Published
- 2018
50. A flexible design for advanced Phase I/II clinical trials with continuous efficacy endpoints
- Author
-
Thomas Jaki and Pavel Mozgunov
- Subjects
Statistics and Probability ,nonmonotonic efficacy ,Biometry ,Endpoint Determination ,Computer science ,Machine learning ,computer.software_genre ,01 natural sciences ,Outcome (game theory) ,010104 statistics & probability ,03 medical and health sciences ,Clinical Trials, Phase II as Topic ,0302 clinical medicine ,Neoplasms ,combination trial ,Advanced phase ,Humans ,Clinical Trials ,Molecular Targeted Therapy ,030212 general & internal medicine ,0101 mathematics ,Clinical Trials, Phase I as Topic ,business.industry ,General Medicine ,Clinical trial ,continuous endpoint ,Artificial intelligence ,Statistics, Probability and Uncertainty ,business ,computer ,Phase I/II clinical trial ,Research Paper - Abstract
There is growing interest in integrated Phase I/II oncology clinical trials involving molecularly targeted agents (MTA). One of the main challenges of these trials are nontrivial dose–efficacy relationships and administration of MTAs in combination with other agents. While some designs were recently proposed for such Phase I/II trials, the majority of them consider the case of binary toxicity and efficacy endpoints only. At the same time, a continuous efficacy endpoint can carry more information about the agent's mechanism of action, but corresponding designs have received very limited attention in the literature. In this work, an extension of a recently developed information‐theoretic design for the case of a continuous efficacy endpoint is proposed. The design transforms the continuous outcome using the logistic transformation and uses an information–theoretic argument to govern selection during the trial. The performance of the design is investigated in settings of single‐agent and dual‐agent trials. It is found that the novel design leads to substantial improvements in operating characteristics compared to a model‐based alternative under scenarios with nonmonotonic dose/combination–efficacy relationships. The robustness of the design to missing/delayed efficacy responses and to the correlation in toxicity and efficacy endpoints is also investigated.
- Published
- 2019
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