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The Cox regression model is widely used in Survival Analysis, also related to medical fields. The Cox regression model is further extended to tackle problems such as non-proportionality and time-dependent covariates. This paper focuses on the behavior of the Cox proportional hazard model in the presence of outliers and time-dependent covariates. To compare the performance of widely used existing time-to-event models in the presence of Outliers and time-dependent covariates and propose a modified Cox model in case of outliers and time-dependent covariates. The algorithm used in the Cox and time-dependent Cox model is extended to tackle the problem of outlier and time-dependent covariates jointly in a model. The estimated model's betas, RMSE, MAE, and MAPE, were compared among the different models. The study concluded that the modified Cox model outperformed the existing time-to-event methodology if the model simultaneously has an Outlier and time-dependent covariates problem.

The Cox regression model is widely used in Survival Analysis, also related to medical fields. The Cox regression model is further extended to tackle problems such as non-proportionality and time-dependent covariates. This paper focuses on the behavior of the Cox proportional hazard model in the presence of outliers and time-dependent covariates. To compare the performance of widely used existing time-to-event models in the presence of Outliers and time-dependent covariates and propose a modified Cox model in case of outliers and time-dependent covariates. The algorithm used in the Cox and time-dependent Cox model is extended to tackle the problem of outlier and time-dependent covariates jointly in a model. The estimated model's betas, RMSE, MAE, and MAPE, were compared among the different models. The study concluded that the modified Cox model outperformed the existing time-to-event methodology if the model simultaneously has an Outlier and time-dependent covariates problem.

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations., Ministerio de Educación y Ciencia, Comunidad de Madrid; Universidad Complutense de Madrid, Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, FALSE, pub

Localization of non-cooperative radio frequency transmitters in wireless networks can be performed by using Time Difference of Arrival (TDoA). However, TDoA measurements are often degraded by limited signal-to-noise ratio, multipath components, non-line-of-sight (NLOS) visibility, etc. This causes the TDoA measurements to have strong outliers, which cannot easily be modelled mathematically. This paper investigates algorithms to perform TDoA-based localization in the presence of outlier measurements. Our algorithm is based on a geometric interpretation, rather than a mathematical, noise model-based approach. We show that our algorithm outperforms existing algorithms when applied to real-world data in a vehicular scenario., info:eu-repo/semantics/published

Presence of missing values and occurrence of outliers in time series cause many hindrances in the analysis of data. Several methods are proposed for determining estimates to replace the missing values and outliers. Mean, median, the largest order statistic and time series model based forecast values are used as the estimates for replacing missing values and outliers. But, no recommendations have been made so far for selection of the estimation methods. This paper attempts to compare the performance of six such estimation methods. Among them, time series models are fitted applying the autoregressive moving average method, long short-term memory method and Facebook’s Prophet method. Models are validated using the test data. Time series of Air Quality Index is used for carrying out for comparative study.

Robust inference for the Cluster Weighted Model requires the specification of a few hyper-parameters. Their role is crucial for increasing the quality of the estimators, while arbitrary decisions about their value could severely hamper inferential results. To guide the user in the delicate choice of such parameters, a monitoring approach has been introduced in the recent literature, yielding an adaptive method. The approach is here exemplified, via the analysis of a dataset on the effect of punishment regimes on crime rates.

The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.

Este documento recoge el proceso seguido para la realización del Trabajo de Fin de Máster, el cual consiste en aprender diferentes modelos de One Class Classification para la clasificación de fallos del eje de una grúa. Se emplearán tanto modelos Shallow como modelos de Deep Learning. Se trabajarán distintas formas de preparar los datos para evaluar el rendimiento de los modelos segun la técnica aplicada: PCA, variables expertas, variables automáticas e imágenes. El objetivo será detectar anomalías y comparar los distintos modelos.

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads., Představujeme novou metodu fuzzy lineární regrese. Metoda je schopna fuzzy aproximovat lineární vztah mezi nezávislou a závislou proměnnou. Očekává se, že nezávislá proměnná bude reálné číslo a že závislá proměnná bude trojúhelníkové fuzzy číslo. Na dvaceti souborech dat demonstrujeme, že metoda je spolehlivá a je méně citlivá na odlehlé hodnoty ve srovnání s fuzzy regresními metodami založenými na pravděpodobnostních metodách. Na rozdíl od jiných běžně používaných fuzzy regresních metod je prezentovaná metoda jednoduchá na implementaci a má lineární časovou složitost. Metoda zaručuje nezápornost rozpětí odhadovaných parametrů modelu.

Statistical inference based on the cluster weighted model often requires some subjective judgment from the modeler. Many features influence the final solution, such as the number of mixture components, the shape of the clusters in the explanatory variables, and the degree of heteroscedasticity of the errors around the regression lines. Moreover, to deal with outliers and contamination that may appear in the data, hyper-parameter values ensuring robust estimation are also needed. In principle, this freedom gives rise to a variety of "legitimate" solutions, each derived by a specific set of choices and their implications in modeling. Here we introduce a method for identifying a "set of good models" to cluster a dataset, considering the whole panorama of choices. In this way, we enable the practitioner, or the scientist who needs to cluster the data, to make an educated choice. They will be able to identify the most appropriate solutions for the purposes of their own analysis, in light of their stability and validity.

Two central objects in constructive approximation, the Christoffel-Darboux kernel and the Christoffel function, are encoding ample information about the associated moment data and ultimately about the possible generating measures. We develop a multivariate theory of the Christoffel-Darboux kernel in C^d, with emphasis on the perturbation of Christoffel functions and their level sets with respect to perturbations of small norm or low rank. The statistical notion of leverage score provides a quantitative criterion for the detection of outliers in large data. Using the refined theory of Bergman orthogonal polynomials, we illustrate the main results, including some numerical simulations, in the case of finite atomic perturbations of area measure of a 2D region. Methods of function theory of a complex variable and (pluri)potential theory are widely used in the derivation of our perturbation formulas.

This thesis was carried out in two parts. The stellar spectral data was used from the Gaia-ESO survey. The data used was fromthe public archive as well as data received from Dr. Recio-Blanco at Observatoire Cote D'Azure. 1) I performed anomaly detection using unsupervised random forests, by applying the concept of weirdness scores to identify outliers. 2) Using spectral data along with physical parameters of objects in the galactic bulge of the Gaia-ESO survey, I built a variational autoencoder neural network to reconstruct stellar spectra and explore latent features learning physical parameters by themselves.

This thesis was carried out in two parts. The stellar spectral data was used from the Gaia-ESO survey. The data used was fromthe public archive as well as data received from Dr. Recio-Blanco at Observatoire Cote D'Azure. 1) I performed anomaly detection using unsupervised random forests, by applying the concept of weirdness scores to identify outliers. 2) Using spectral data along with physical parameters of objects in the galactic bulge of the Gaia-ESO survey, I built a variational autoencoder neural network to reconstruct stellar spectra and explore latent features learning physical parameters by themselves.

Cardiac troponins (cTn) are the preferred biomarkers for the evaluation of myocardial injury and play a key role in the diagnosis of acute myocardial infarction (MI). Pre-analytical or analytical issues and interferences affecting troponin T and I assays are therefore of major concern given the risk of misdiagnosis. False positive troponin results have been related to various interferences including anti-troponin antibodies, heterophilic antibodies, or elevated alkaline phosphatase level. On the other hand, false negative results have been reported in the case of a large biotin intake. These interferences are characterized with erroneous but reproducible troponin results. Of interest, non-reproducible results have also been reported in the literature. In other words, if the sample is reanalyzed a second time, a significant difference in troponin results will be observed. These interferences have been named "fliers" or "outliers". Compared to the biotin interference that received major attention in the literature, troponin outliers are also able to induce harmful clinical consequences for the patient. Moreover, the prevalence of outliers in recent studies was found to be higher (0.28-0.57%) compared to the biotin interference. The aim of this systematic review is to warn clinicians about these non-reproducible results that may alter their clinical judgment. Four case reports that occurred in the Clinique of Saint-Luc Bouge are presented to attest this point. Moreover, we aimed at identifying the nature of these non-reproducible troponin results, determining their occurrence, and describing the best way for their identification.

Jump-diffusion processes often model financial time series as they can simulate the random jumps that they frequently exhibit. These jumps can be seen as anomalies and are essential for financial analysis and model building, making them vital to detect.The realized variation, realized bipower variation, and realized semi-variation were tested to see if one could use them to detect jumps in a jump-diffusion process and if anomaly detection algorithms can use them as features to improve their accuracy. The algorithms tested were Isolation Forest, Robust Random Cut Forest, and Isolation Forest Algorithm for Streaming Data, where the latter two use streaming data. This was done by generating a Merton jump-diffusion process with a varying jump-rate and tested using each algorithm with each of the features. The performance of each algorithm was measured using the F1-score to compare the difference between features and algorithms. It was found that the algorithms were improved from using the features; Isolation Forest saw improvement from using one, or more, of the named features. For the streaming algorithms, Robust Random Cut Forest performed the best for every jump-rate except the lowest. Using a combination of the features gave the highest F1-score for both streaming algorithms. These results show one can use these features to extract jumps, as anomaly scores, and improve the accuracy of the algorithms, both in a batch and stream setting., Hopp-diffusionsprocesser används regelbundet för att modellera finansiella tidsserier eftersom de kan simulera de slumpmässiga hopp som ofta uppstår. Dessa hopp kan ses som anomalier och är viktiga för finansiell analys och modellbyggnad, vilket gör dom väldigt viktiga att hitta. Den realiserade variationen, realiserade bipower variationen, och realiserade semi-variationen är faktorer av en tidsserie som kan användas för att hitta hopp i hopp-diffusionprocesser. De används här för att testa om anomali-detektionsalgoritmer kan använda funktionerna för att förbättra dess förmåga att detektera hopp. Algoritmerna som testades var Isolation Forest, Robust Random Cut Forest, och Isolation Forest Algoritmen för Strömmande data, där de två sistnämnda använder strömmande data. Detta gjordes genom att genera data från en Merton hopp-diffusionprocess med varierande hoppfrekvens där de olika algoritmerna testades med varje funktion samt med kombinationer av funktioner. Prestationen av varje algoritm beräknades med hjälp av F1-värde för att kunna jämföra algoritmerna och funktionerna med varandra. Det hittades att funktionerna kan användas för att extrahera hopp från hopp-diffusionprocesser och även använda de som en indikator för när hopp skulle ha hänt. Algoritmerna fick även ett högre F1-värde när de använde funktionerna. Isolation Forest fick ett förbättrat F1-värde genom att använda en eller fler utav funktionerna och hade ett högre F1-värde än att bara använda funktionerna för att detektera hopp. Robust Random Cut Forest hade högst F1-värde av de två algoritmer som använde strömmande data och båda fick högst F1-värde när man använde en kombination utav alla funktioner. Resultatet visar att dessa funktioner fungerar för att extrahera hopp från hopprocesser, använda dem för att detektera hopp, och att algoritmernas förmåga att detektera hoppen ökade med hjälp av funktionerna.

Statistical inference based on the cluster weighted model often requires some subjective judgment from the modeler. Many features influence the final solution, such as the number of mixture components, the shape of the clusters in the explanatory variables, and the degree of heteroscedasticity of the errors around the regression lines. Moreover, to deal with outliers and contamination that may appear in the data, hyper-parameter values ensuring robust estimation are also needed. In principle, this freedom gives rise to a variety of “legitimate” solutions, each derived by a specific set of choices and their implications in modeling. Here we introduce a method for identifying a “set of good models” to cluster a dataset, considering the whole panorama of choices. In this way, we enable the practitioner, or the scientist who needs to cluster the data, to make an educated choice. They will be able to identify the most appropriate solutions for the purposes of their own analysis, in light of their stability and validity.

Binary outcomes are extremely common in biomedical research. Despite its popularity, binomial regression often fails to model this kind of data accurately due to the overdispersion problem. Many alternatives can be found in the literature, the beta-binomial (BB) regression model being one of the most popular. The additional parameter of this model enables a better fit to overdispersed data. It also exhibits an attractive interpretation in terms of the intraclass correlation coefficient. Nonetheless, in many real data applications, a single additional parameter cannot handle the entire excess of variability. In this study, we propose a new finite mixture distribution with BB components, namely, the flexible beta-binomial (FBB), which is characterized by a richer parameterization. This allows us to enhance the variance structure to account for multiple causes of overdispersion while also preserving the intraclass correlation interpretation. The novel regression model, based on the FBB distribution, exploits the flexibility and large variety of the distribution's possible shapes (which includes bimodality and various tail behaviors). Thus, it succeeds in accounting for several (possibly concomitant) sources of overdispersion stemming from the presence of latent groups in the population, outliers, and excessive zero observations. Adopting a Bayesian approach to inference, we perform an intensive simulation study that shows the superiority of the new regression model over that of the existing ones. Its better performance is also confirmed by three applications to real datasets extensively studied in the biomedical literature, namely, bacteria data, atomic bomb radiation data, and control mice data.

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respec-tively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respec-tively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.

Jump-diffusion processes often model financial time series as they can simulate the random jumps that they frequently exhibit. These jumps can be seen as anomalies and are essential for financial analysis and model building, making them vital to detect.The realized variation, realized bipower variation, and realized semi-variation were tested to see if one could use them to detect jumps in a jump-diffusion process and if anomaly detection algorithms can use them as features to improve their accuracy. The algorithms tested were Isolation Forest, Robust Random Cut Forest, and Isolation Forest Algorithm for Streaming Data, where the latter two use streaming data. This was done by generating a Merton jump-diffusion process with a varying jump-rate and tested using each algorithm with each of the features. The performance of each algorithm was measured using the F1-score to compare the difference between features and algorithms. It was found that the algorithms were improved from using the features; Isolation Forest saw improvement from using one, or more, of the named features. For the streaming algorithms, Robust Random Cut Forest performed the best for every jump-rate except the lowest. Using a combination of the features gave the highest F1-score for both streaming algorithms. These results show one can use these features to extract jumps, as anomaly scores, and improve the accuracy of the algorithms, both in a batch and stream setting., Hopp-diffusionsprocesser används regelbundet för att modellera finansiella tidsserier eftersom de kan simulera de slumpmässiga hopp som ofta uppstår. Dessa hopp kan ses som anomalier och är viktiga för finansiell analys och modellbyggnad, vilket gör dom väldigt viktiga att hitta. Den realiserade variationen, realiserade bipower variationen, och realiserade semi-variationen är faktorer av en tidsserie som kan användas för att hitta hopp i hopp-diffusionprocesser. De används här för att testa om anomali-detektionsalgoritmer kan använda funktionerna för att förbättra dess förmåga att detektera hopp. Algoritmerna som testades var Isolation Forest, Robust Random Cut Forest, och Isolation Forest Algoritmen för Strömmande data, där de två sistnämnda använder strömmande data. Detta gjordes genom att genera data från en Merton hopp-diffusionprocess med varierande hoppfrekvens där de olika algoritmerna testades med varje funktion samt med kombinationer av funktioner. Prestationen av varje algoritm beräknades med hjälp av F1-värde för att kunna jämföra algoritmerna och funktionerna med varandra. Det hittades att funktionerna kan användas för att extrahera hopp från hopp-diffusionprocesser och även använda de som en indikator för när hopp skulle ha hänt. Algoritmerna fick även ett högre F1-värde när de använde funktionerna. Isolation Forest fick ett förbättrat F1-värde genom att använda en eller fler utav funktionerna och hade ett högre F1-värde än att bara använda funktionerna för att detektera hopp. Robust Random Cut Forest hade högst F1-värde av de två algoritmer som använde strömmande data och båda fick högst F1-värde när man använde en kombination utav alla funktioner. Resultatet visar att dessa funktioner fungerar för att extrahera hopp från hopprocesser, använda dem för att detektera hopp, och att algoritmernas förmåga att detektera hoppen ökade med hjälp av funktionerna.

Selection by breeders modifies the morphology, behaviour and performance of domesticated species. Here, we examined signs of selection in Finnhorse, the only native horse breed in Finland. We first searched divergent genomic regions between Finnhorses and other breeds, as well as between different breeding sections of the Finnhorse with data from Illumina Equine SNP70 BeadChip, and then studied several of the detected regions in more detail. We found altogether 35 common outlier SNPs between Finnhorses and other breeds using two different selection tests. Many of the SNPs were located close to genes affecting coat colour, performance, size, sugar metabolism, immune response and olfaction. We selected genes affecting coat colour (KIT, MITF, PMEL), performance (MSTN) and locomotion (DMRT3) for a more detailed examination. In addition, we looked for, and found, associations with height at withers and SNPs located close to gene LCORL. Among the four breeding sections of Finnhorses (harness trotters, riding horses, draught horses and pony‐sized horses), a single SNP located close to the DMRT3 gene was significantly differentiated and only between harness trotters and pony‐sized horses.

Two central objects in constructive approximation, the Christoffel-Darboux kernel and the Christoffel function, are encoding ample information about the associated moment data and ultimately about the possible generating measures. We develop a multivariate theory of the Christoffel-Darboux kernel in C^d, with emphasis on the perturbation of Christoffel functions and their level sets with respect to perturbations of small norm or low rank. The statistical notion of leverage score provides a quantitative criterion for the detection of outliers in large data. Using the refined theory of Bergman orthogonal polynomials, we illustrate the main results, including some numerical simulations, in the case of finite atomic perturbations of area measure of a 2D region. Methods of function theory of a complex variable and (pluri)potential theory are widely used in the derivation of our perturbation formulas.

Background Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes. Here, we describe an R package, called ORdensity, that implements a recent methodology (Irigoien and Arenas, 2018) developed in order to identify differentially expressed genes. The benefits of parallel implementation are discussed. Results ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification. Conclusions The new package, ORdensity, offers a friendly and easy way to identify differentially expressed genes, which is very useful for users not familiar with programming. Availability https://github.com/rsait/ORdensity

Background Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes. Here, we describe an R package, called ORdensity, that implements a recent methodology (Irigoien and Arenas, 2018) developed in order to identify differentially expressed genes. The benefits of parallel implementation are discussed. Results ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification. Conclusions The new package, ORdensity, offers a friendly and easy way to identify differentially expressed genes, which is very useful for users not familiar with programming. Availability https://github.com/rsait/ORdensity

Introduction: The number of patients treated in inadequate hospital wards is increasing. Elderly and fragile patients with medical conditions are particularly vulnerable for being placed in inadequate hospital wards. They also run a twice as high risk of health-related complications and increased mortality. RNs obtain specific knowledge by working on specific wards. However, RNs’ experience of caring for outliers seems relatively unexplored, as few studies explore RNs’ experiences in giving nursing care to outliers. Aim: The aim of the study was to explore RNs’ experience of giving care to outliers with medical conditions at a university hospital in Sweden. Method: An interview study with 14 RNs from medical and surgical wards were conducted by semi-structured interviews. The inclusion criteria were that they had worked as an RN for minimum two years and had experience of outliers. Data were collected between December 2016 and January 2018. The interviews were transcribed and analyzed using conventional qualitative content analysis to identify categories and themes according to the aim of the study. Result: One overarching theme emerged: The interviewed nurses experienced that outliers’ medical and nursing care was delayed and therefore hospitalization was prolonged. The informants’ experience is substantiated by five main categories: Inadequate information from ER to the ward leads to concern, Nursing interventions are performed later and lead to a sense of powerlessness, Unavailable drugs lead to delayed or no drug administration, Patients on inadequate wards do not receive proper information, and The RN does not know when the patient is ready for discharge planning. Conclusion: RNs described their experience of caring for outliers as an obstacle course and that this risks prolonged hospitalization. They are not equipped to make the most appropriate decisions, as they have other specific knowledge than the specific needs of outliers, and as a result, care

For high-dimensional data, it is a tedious task to determine anomalies such as outliers. We present a novel outlier detection method for high-dimensional contingency tables. We use the class of decomposable graphical models to model the relationship among the variables of interest, which can be depicted by an undirected graph called the interaction graph. Given an interaction graph, we derive a closed-form expression of the likelihood ratio test (LRT) statistic and an exact distribution for efficient simulation of the test statistic. An observation is declared an outlier if it deviates significantly from the approximated distribution of the test statistic under the null hypothesis. We demonstrate the use of the LRT outlier detection framework on genetic data modeled by Chow–Liu trees.

Introduction: The number of patients treated in inadequate hospital wards is increasing. Elderly and fragile patients with medical conditions are particularly vulnerable for being placed in inadequate hospital wards. They also run a twice as high risk of health-related complications and increased mortality. RNs obtain specific knowledge by working on specific wards. However, RNs’ experience of caring for outliers seems relatively unexplored, as few studies explore RNs’ experiences in giving nursing care to outliers. Aim: The aim of the study was to explore RNs’ experience of giving care to outliers with medical conditions at a university hospital in Sweden. Method: An interview study with 14 RNs from medical and surgical wards were conducted by semi-structured interviews. The inclusion criteria were that they had worked as an RN for minimum two years and had experience of outliers. Data were collected between December 2016 and January 2018. The interviews were transcribed and analyzed using conventional qualitative content analysis to identify categories and themes according to the aim of the study. Result: One overarching theme emerged: The interviewed nurses experienced that outliers’ medical and nursing care was delayed and therefore hospitalization was prolonged. The informants’ experience is substantiated by five main categories: Inadequate information from ER to the ward leads to concern, Nursing interventions are performed later and lead to a sense of powerlessness, Unavailable drugs lead to delayed or no drug administration, Patients on inadequate wards do not receive proper information, and The RN does not know when the patient is ready for discharge planning. Conclusion: RNs described their experience of caring for outliers as an obstacle course and that this risks prolonged hospitalization. They are not equipped to make the most appropriate decisions, as they have other specific knowledge than the specific needs of outliers, and as a result, care

The detection of outliers in the field of data mining (DM) and the process of knowledge discovery in databases (KDD) is of great interest in areas that require support systems for decision making. A straightforward application can be found in the financial area, where DM can potentially detect financial fraud or find errors produced by the users. Thus, it is essential to evaluate the veracity of the information, through the use of methods for the detection of unusual behaviors in the data. This paper proposes a method to detect values ​​that are considered outliers in a database of nominal type data. The method implements a global algorithm of "k" closest neighbors, a clustering algorithm called k-means and a statistical method called chi-square. These techniques have been implemented on a database of clients who have requested a financial credit. The experiment was performed on a data set with 1180 tuples, where, outliers were deliberately introduced. The results showed that the proposed method is able to detect all the outliers entered.

The sample skewness and kurtosis of macroeconomic and financial time series are routinely scrutinized in the early stages of model-building and are often the central topic of studies in economics and finance. Notwithstanding the availability of several robust estimators, most scholars in economics rely on method-of-moments estimation that is known to be very sensitive to outliers. We carry out an extensive Monte Carlo analysis to evaluate the bias and root mean squared error of twelve different estimators of skewness and kurtosis. We consider nine statistical distributions that approximate the range of data generating processes of many macroeconomic and financial time series. Both in independently and identically distributed samples and in data generating processes featuring serial correlation L-moments and trimmed L-moments estimators are particularly resistant to outliers and deliver improvements over standard as well as alternative robust estimators of skewness and kurtosis. The application to 128 macroeconomic and financial time series sourced from a large, monthly frequency, database (i.e. the FRED-MD of McCracken and Ng, 2016) confirms the findings of the simulation study.

The detection of outliers in the field of data mining (DM) and the process of knowledge discovery in databases (KDD) is of great interest in areas that require support systems for decision making. A straightforward application can be found in the financial area, where DM can potentially detect financial fraud or find errors produced by the users. Thus, it is essential to evaluate the veracity of the information, through the use of methods for the detection of unusual behaviors in the data. This paper proposes a method to detect values ​​that are considered outliers in a database of nominal type data. The method implements a global algorithm of "k" closest neighbors, a clustering algorithm called k-means and a statistical method called chi-square. These techniques have been implemented on a database of clients who have requested a financial credit. The experiment was performed on a data set with 1180 tuples, where, outliers were deliberately introduced. The results showed that the proposed method is able to detect all the outliers entered.

Situating this art practice as data art in the field of digital art, I suggest a new landscape of data art by exploring data as an art material, medium, a concept-driver and as containing the current social-political issues of data bias. The creative outcomes of this research project result from my process of artistic exploration of data. This has been a journey that explored the context of data and artistic potential of outliers as a concept-driver.

Background Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes. Here, we describe an R package, called ORdensity, that implements a recent methodology (Irigoien and Arenas, 2018) developed in order to identify differentially expressed genes. The benefits of parallel implementation are discussed. Results ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification. Conclusions The new package, ORdensity, offers a friendly and easy way to identify differentially expressed genes, which is very useful for users not familiar with programming. Availability https://github.com/rsait/ORdensity

Avui en dia les empreses cerquen aplicar processos que permetin disminuir al màxim el consum de recursos, tant per motius econòmics com per raons mediambientals. Un altre aspecteque es preténreduir és el temps destinat a realitzar aquestes tasques. L’espectroscòpia NIR és un exemple de tecnologia aplicada a l’anàlisi de mostres que no genera residus i a méspermet obtenir un espectre i extreure’n resultats en menys d’un minut.L’objectiu principal d’aquest projecte ha estat construir un model quantitatiu que permeti determinar l’acidesa d’una mostra de resina de recobriment en pols a partir del seu espectre NIR. Per poder-lo obtenir,ha calgut analitzar prèviament un elevat nombre de mostres iavaluarels resultats amb paràmetres estadístics obtinguts mitjançant un software especialitzat (OPUS Quant). La part experimental d’aquest TFG es va desenvolupar al laboratori de qualitat i producció de la planta de l’empresa DSM situada a Santa Margarida i els Monjos, a l’Alt Penedès, de forma vinculada a un conveni de pràctiques. Cada 1,5-2 mesos s’ha realitzat una nova versió del model inicial amb les mostresacumuladesque s’havien analitzat fins aquell moment, amb l’objectiu de millorar el model anterior amb noves dades i obtenir millors paràmetres estadístics, intentant descartar el menor nombre d’espectres amb valors atípics possibles.Finalment s’ha obtingut un model que té en compte fins a 2.204 espectres de més de 1.000 mostres diferents, amb una desviació típica acceptable (0,623) per a l’aplicació per a la que es té en compte i havent descartatun 6,13% de les mostres analitzades.

Multiclass problem has continued to be an active research area due to the challenges paused by the issue of imbalance datasets and lack of a unifying classification algorithms. Real world problems are of multiclass nature with skewed representations. The study focused on the challenges of multiclass classification. Multiclass datasets were adopted from UCI machine learning repository. The research developed a heterogeneous ensemble model for multiclass classification and outlier detection that combined several strategies and ensemble techniques. Preprocessing involved filtering global outliers and resampling datasets using synthetic minority oversampling technique (SMOTE) algorithm. Datasets binarization was done using OnevsOne decomposing technique. Heterogeneous ensemble model was constructed using adaboost, random subspace algorithms and random forest as the base classifier. The classifiers built were combined using average of probabilities voting rule and evaluated using 10 fold stratified cross validation. The model showed better performance in terms of outlier detection and classification prediction for multiclass problem. The model outperformed other commonly used classical algorithms. The study findings established proper preprocessing and decomposing multiclass results in an improved performance of minority outlier classes while safe guarding integrity of the majority classes.

Biomedical research often features continuous responses bounded by the interval [0, 1]. The well-known beta regression model addresses the constrained nature of these data, while its augmented and mixed-effects variants can address the presence of zeros and/or ones and longitudinal or clustered response values, respectively. However, these models are not robust to the presence of outliers and/or excessive number of observations near the tails. We propose a new augmented mixed-effects regression model based on a special beta mixture distribution that is capable of handling these issues. Extensive simulation studies show the superiority of the proposed model to the models most often used in the literature. The proposed model is applied to two real datasets: one taken from a long-term study of Parkinson's disease and the other taken from a study on reading accuracy.

This tech note extends the discussion of the numericalresults in [1]. The main article should be read first. Thatarticle presents RAPS for nonlinear applications; therefore,linear systems are a special case. This technical note includesnumerical results specific to linear systems that did not fitwithin the journal page constraints. Section I briefly introduceslinear system mode model. Section III studies the performanceof the binary RAPS approach for vehicle state estimation. Thelinear application is referred to in the GNSS literature as a PVAmodel wherein the GNSS measurements are used to estimatethe position, velocity, and acceleration of the GNSS antenna.Such estimators are included in almost all GNSS receivers.

Many applications, including connected and autonomous vehicles, would benefit from navigation technologies reliablyachieving sub-meter position accuracy with high reliability on moving platforms. Commercial on-vehicle implementation ofEarth-referenced positioning at submeter accuracy with 99% probability would require widely and reliably available differentialcorrections; however, such corrections delivered on a nationwide or global scale via satellite systems will incur latency betweentheir time-of-applicability and their time-of-reception at the vehicle.Phase 1 of this project presented a differential correction computation methodology designed to be robust to latency andstudied position accuracy as a function of differential correction latency for stationary receivers [1]–[3]. The study showed thatsubmeter accuracy at 95% probability was achievable when a sufficient number and diversity of satellites were available.This report summarizes the conclusions of the Phase 2 of the work performed by University of California, Riverside (UCR).There were two main goals for this effort. For moving platforms, Phase 2 investigates:1) the feasibility of achieving meter-level positioning accuracy on at least 95% of epochs using Global Navigation SatelliteSystem (DGNSS) based state estimation; and,2) the sensitivity of that positioning accuracy to communication latency.The study uses the utilizes the local based station design presented in [1].The study presents and experimentally analyzes two state estimation approaches suitable for moving platforms. The Position,Velocity, Acceleration (PVA) approach uses DGNSS data only within a Kalman filter framework. The Inertial Navigation System(INS) approach uses DGNSS and inertial measurement data within an extended Kalman filter implementation. Section VIIshows that both approaches have performance exceeding the SAE J2945 specification (1.5 meter horizontal accuracy and 3.0meter vertical accuracy at 68%) with PVA achieving 1m horizonta

The sample skewness and kurtosis of macroeconomic and financial time series are routinely scrutinized in the early stages of model-building and are often the central topic of studies in economics and finance. Notwithstanding the availability of several robust estimators, most scholars in economics rely on method-of-moments estimation that is known to be very sensitive to outliers. We carry out an extensive Monte Carlo analysis to compare the bias and root mean squared error of twelve different estimators of skewness and kurtosis. We consider nine statistical distributions that approximate the range of data generating processes of many macroeconomic and financial time series. Both in independently and identically distributed samples and in data generating processes featuring serial correlation L-moments and trimmed L-moments estimators are particularly resistant to outliers and deliver the lowest root mean squared error. The application to 128 macroeconomic and financial time series sourced from a large, monthly frequency, database (i.e. the FRED-MD of McCracken and Ng, 2016) confirms the findings of the simulation study.

In statistics and data science, outliers are data points that differ greatly from other observations in a data set. They are important attributes of the data because they can dramatically influence patterns and relationships manifested by non-outliers. It is therefore very important to detect and adequately deal with outliers. Recently, a novel algorithm, the ROMA algorithm, has been proposed [11]. In this paper, we propose a modification of the ROMA algorithm that reduces its computational complexity from $O(n^2 m)$ to $O((n/(2^m-o(1)))^2 m)$ where $n$ is the number of data points and $m$ is the dimension of the space. And as a consequence, if $\log(n) <2^m$, then the improved complexity is $O((n/\log(n))^2 m)$.

The sample skewness and kurtosis of macroeconomic and financial time series are routinely scrutinized in the early stages of model-building and are often the central topic of studies in economics and finance. Notwithstanding the availability of several robust estimators, most scholars in economics rely on method-of-moments estimation that is known to be very sensitive to outliers. We carry out an extensive Monte Carlo analysis to compare the bias and root mean squared error of twelve different estimators of skewness and kurtosis. We consider nine statistical distributions that approximate the range of data generating processes of many macroeconomic and financial time series. Both in independently and identically distributed samples and in data generating processes featuring serial correlation L-moments and trimmed L-moments estimators are particularly resistant to outliers and deliver the lowest root mean squared error. The application to 128 macroeconomic and financial time series sourced from a large, monthly frequency, database (i.e. the FRED-MD of McCracken and Ng, 2016) confirms the findings of the simulation study.

This study investigates how outliers of different magnitude impact the robustness of the twosample t-test. A simulation study approach is used to analyze the behavior of type I error rates when outliers are added to generated data. Outliers may distort parameter estimates such as the mean and variance and cause misleading test results. Previous research has shown that Welch’s ttest performs better than the traditional Student’s t-test when group variances are unequal. Therefore these two alternative statistics are compared in terms of type I error rates when outliers are added to the samples. The results show that control of type I error rates can be maintained in the presence of a single outlier. Depending on the magnitude of the outlier and the sample size, there are scenarios where the t-test is robust. However, the sensitivity of the t-test is illustrated by deteriorating type I error rates when more than one outlier are included. The comparison between Welch’s t-test and Student’s t-test shows that the former is marginally more robust against outlier influence.

Biomedical research often features continuous responses bounded by the interval [0, 1]. The well-known beta regression model addresses the constrained nature of these data, while its augmented and mixed-effects variants can address the presence of zeros and/or ones and longitudinal or clustered response values, respectively. However, these models are not robust to the presence of outliers and/or excessive number of observations near the tails. We propose a new augmented mixed-effects regression model based on a special beta mixture distribution that is capable of handling these issues. Extensive simulation studies show the superiority of the proposed model to the models most often used in the literature. The proposed model is applied to two real datasets: one taken from a long-term study of Parkinson's disease and the other taken from a study on reading accuracy.

Multiclass problem has continued to be an active research area due to the challenges paused by the issue of imbalance datasets and lack of a unifying classification algorithms. Real world problems are of multiclass nature with skewed representations. The study focused on the challenges of multiclass classification. Multiclass datasets were adopted from UCI machine learning repository. The research developed a heterogeneous ensemble model for multiclass classification and outlier detection that combined several strategies and ensemble techniques. Preprocessing involved filtering global outliers and resampling datasets using synthetic minority oversampling technique (SMOTE) algorithm. Datasets binarization was done using OnevsOne decomposing technique. Heterogeneous ensemble model was constructed using adaboost, random subspace algorithms and random forest as the base classifier. The classifiers built were combined using average of probabilities voting rule and evaluated using 10 fold stratified cross validation. The model showed better performance in terms of outlier detection and classification prediction for multiclass problem. The model outperformed other commonly used classical algorithms. The study findings established proper preprocessing and decomposing multiclass results in an improved performance of minority outlier classes while safe guarding integrity of the majority classes.

Multiclass problem has continued to be an active research area due to the challenges paused by the issue of imbalance datasets and lack of a unifying classification algorithms. Real world problems are of multiclass nature with skewed representations. The study focused on the challenges of multiclass classification. Multiclass datasets were adopted from UCI machine learning repository. The research developed a heterogeneous ensemble model for multiclass classification and outlier detection that combined several strategies and ensemble techniques. Preprocessing involved filtering global outliers and resampling datasets using synthetic minority oversampling technique (SMOTE) algorithm. Datasets binarization was done using OnevsOne decomposing technique. Heterogeneous ensemble model was constructed using adaboost, random subspace algorithms and random forest as the base classifier. The classifiers built were combined using average of probabilities voting rule and evaluated using 10 fold stratified cross validation. The model showed better performance in terms of outlier detection and classification prediction for multiclass problem. The model outperformed other commonly used classical algorithms. The study findings established proper preprocessing and decomposing multiclass results in an improved performance of minority outlier classes while safe guarding integrity of the majority classes.

This study investigates how outliers of different magnitude impact the robustness of the twosample t-test. A simulation study approach is used to analyze the behavior of type I error rates when outliers are added to generated data. Outliers may distort parameter estimates such as the mean and variance and cause misleading test results. Previous research has shown that Welch’s ttest performs better than the traditional Student’s t-test when group variances are unequal. Therefore these two alternative statistics are compared in terms of type I error rates when outliers are added to the samples. The results show that control of type I error rates can be maintained in the presence of a single outlier. Depending on the magnitude of the outlier and the sample size, there are scenarios where the t-test is robust. However, the sensitivity of the t-test is illustrated by deteriorating type I error rates when more than one outlier are included. The comparison between Welch’s t-test and Student’s t-test shows that the former is marginally more robust against outlier influence.

The outlier detection in the field of data mining and Knowledge Discovering from Data (KDD) is capturing special interest due to its benefits. It can be applied in the financial area; because the obtained data patterns can help finding possible frauds and user errors. Therefore, it is essential to assess the truthfulness of the information. In this context, data auditory process uses techniques of data mining that play a significant role in the detection of unusual behavior. Here, a method for detecting values that can be considered as outliers in a nominal database is proposed. The basic idea in this method is to implement: a Global k-Nearest Neighbors algorithm, a clustering algorithm named k-means, and a statistical method of chi-square. The application of algorithms has been developed with a database of candidate people for the granting of a loan. Each test was made on a dataset of 1180 registers in which outliers have been introduced deliberately. The experimental results show that the method is able to detect all introduced values, which were previously labeled to be differentiated. Consequently, there were found a total of 48 tuples with outliers of 11 nominal columns. © 2019 IEEE.

Single photon lidars (in solid state form) offer several benefits over pulsed lidars, such as independence of micro-mechanical moving parts or rotating joints, lower power consumption, faster acquisition rate, and reduced size. When mass produced, they will be cheaper and smaller and thus very attractive for mobile laser scanning applications. However, as these lidars operate by receiving single photons, they are very susceptible to background illumination such as sunlight. In other words, the observations contain a significant amount of noise, or to be specific, outliers. This causes trouble for measurements done in motion, as the sampling rate (i.e. the measurement frequency) should be low and high at the same time. It should be low enough so that target detection is robust, meaning that the targets can be distinguished from the single-photon avalanche diode (SPAD) triggings caused by the background photons. On the other hand, the sampling rate should be high enough to allow for measurements to be done from motion. Quick sampling reduces the probability that a sample gathered during motion would contain data from more than a single target at a specific range. Here, we study the exploitation of spatial correlations that exist between the observations as a mean to overcome this sampling rate paradox. We propose computational methods for short and long range. Our results indicate that the spatial correlations do indeed allow for faster and more robust sampling of measurements, which makes single photon lidars more attractive in (daylight) mobile laser scanning.

This is an Accepted Manuscript of an article published by Taylor &Francis in “ Journal of applied statistics” on 17 Dec 2019 ,available online: https://www.tandfonline.com/doi/abs/10.1080/02664763.2019.1702929?journalCode=cjas20, Metric multidimensional scaling (MDS) is a widely used multivariate method with applications in almost all scientific disciplines. Eigenvalues obtained in the analysis are usually reported in order to calculate the overall goodness-of-fit of the distance matrix. In this paper, we refine MDS goodness-of-fit calculations, proposing additional point and pairwise goodness-of-fit statistics that can be used to filter poorly represented observations in MDS maps. The proposed statistics are especially relevant for large data sets that contain outliers, with typically many poorly fitted observations, and are helpful for improving MDS output and emphasizing the most important features of the dataset. Several goodness-of-fit statistics are considered, and both Euclidean and non-Euclidean distance matrices are considered. Some examples with data from demographic, genetic and geographic studies are shown., Peer Reviewed, Postprint (author's final draft)

Many applications, including connected and autonomous vehicles, would benefit from navigation technologies reliablyachieving sub-meter position accuracy with high reliability on moving platforms. Commercial on-vehicle implementation ofEarth-referenced positioning at submeter accuracy with 99% probability would require widely and reliably available differentialcorrections; however, such corrections delivered on a nationwide or global scale via satellite systems will incur latency betweentheir time-of-applicability and their time-of-reception at the vehicle.Phase 1 of this project presented a differential correction computation methodology designed to be robust to latency andstudied position accuracy as a function of differential correction latency for stationary receivers [1]–[3]. The study showed thatsubmeter accuracy at 95% probability was achievable when a sufficient number and diversity of satellites were available.This report summarizes the conclusions of the Phase 2 of the work performed by University of California, Riverside (UCR).There were two main goals for this effort. For moving platforms, Phase 2 investigates:1) the feasibility of achieving meter-level positioning accuracy on at least 95% of epochs using Global Navigation SatelliteSystem (DGNSS) based state estimation; and,2) the sensitivity of that positioning accuracy to communication latency.The study uses the utilizes the local based station design presented in [1].The study presents and experimentally analyzes two state estimation approaches suitable for moving platforms. The Position,Velocity, Acceleration (PVA) approach uses DGNSS data only within a Kalman filter framework. The Inertial Navigation System(INS) approach uses DGNSS and inertial measurement data within an extended Kalman filter implementation. Section VIIshows that both approaches have performance exceeding the SAE J2945 specification (1.5 meter horizontal accuracy and 3.0meter vertical accuracy at 68%) with PVA achieving 1m horizonta

This tech note extends the discussion of the numericalresults in [1]. The main article should be read first. Thatarticle presents RAPS for nonlinear applications; therefore,linear systems are a special case. This technical note includesnumerical results specific to linear systems that did not fitwithin the journal page constraints. Section I briefly introduceslinear system mode model. Section III studies the performanceof the binary RAPS approach for vehicle state estimation. Thelinear application is referred to in the GNSS literature as a PVAmodel wherein the GNSS measurements are used to estimatethe position, velocity, and acceleration of the GNSS antenna.Such estimators are included in almost all GNSS receivers.