Este trabalho apresenta um estudo de inferência e diagnósticos em modelos assimétricos. A análise de influência é baseada na metodologia para modelos com dados incompletos, que é relacionada ao algoritmo EM (Zhu e Lee, 2001). Além dos modelos de regressão Normal Assimétrico (Azzalini, 1999) e t-Normal Assimétrico (Gómez, Venegas e Bolfarine, 2007) existentes, são desenvolvidas duas novas classes de modelos, denominados modelos de misturas de escala normal assimétricos (englobando as distribuições Normal, t-Normal, Slash, Normal-Contaminada e Exponencial-potência Assimétricas) e modelos lineares mistos robustos assimétricos, utilizando distribuições de misturas de escalas normais assimétricas para o efeito aleatório e distribuições de misturas de escalas para o erro aleatório. Para o modelo misto, a matriz de informação de Fisher observada é calculada utilizando a aproximação de Louis (1982) para dados incompletos. Para todos os modelos, algoritmos tipo EM são desenvolvidos de forma a fornecer uma solução numérica para os parâmetros dos modelos de regressão. Para cada modelo de regressão, medidas de bondade de ajuste são realizadas via inspeção visual do gráfico de envelope simulado. Para os modelos de misturas de escalas normais assimétricos, um estudo de robustez do algoritmo EM proposto é desenvolvido, determinando a eficácia dos estimadores apresentados. Aplicações dos modelos estudados são realizadas para os conjuntos de dados do Australian Institute of Sports (AIS), para o conjunto de dados sobre qualidade de vida de pacientes (mulheres) com câncer de mama, em um estudo realizado pelo Centro de Atenção Integral à Saúde da Mulher (CAISM) em conjunto com a Faculdade de Ciências Médicas, da Universidade Estadual de Campinas e para o conjunto de dados de colesterol de Framingham. / This work presents a study of inference and diagnostic in asymmetric models. The influence analysis is based in the methodology for models with incomplete data, that is related to the algorithm EM (Zhu and Lee, 2001). Beyond of the existing asymmetric normal (Azzalini, 1999) and t-Normal asymmetric (Gómez, Venegas and Bolfarine, 2007) regression models, are developed two new classes of models, namely asymmetric normal scale mixture models (embodying the asymmetric Normal, t-Normal, Slash, Contaminated-Normal and Power-Exponential distributions) and asymmetric robust linear mixed models, utilizing asymmetric normal scale mixture distributions for the random effect and normal scale mixture distributions for the random error. For the mixed model, the observed Fisher information matrix is calculated using the Louis\' (1982) approach for incomplete data. For all models, EM algorithms are developed, that provide a numeric solution for the parameters of the regression models. For each regression model, measures of goodness of fit are realized through visual inspection of the graphic of simulated envelope. For the asymmetric normal scale mixture models, a study of robustness of the proposed EM algorithm is developed to determine the efficacy of the presented estimators. Applications of the studied models are made for the data set of the Australian Institute of Sports (AIS), for the data set about quality of life of patients (women) with breast cancer, in a study made by Centro de Atenção Integral à Saúde da Mulher (CAISM) in conjoint with the Medical Sciences Faculty, of the Campinas State\'s University and for the data set of Framingham\'s cholesterol study.

Apresentamos algumas aplicações de ferramentas estatísticas que são comumente utilizadas na melhoria da qualidade de processos industriais. Inicialmente, desenvolveu-se procedimentos para testar a competência de laboratórios que participam de programas de ensaios de proficiência. Em situações onde os laboratórios medem várias vezes no mesmo ponto, utilizou-se o modelo de erros de medição, proposto por Jaech [39](1985). Além disso, a inferência sobre os parâmetros de tendência aditiva foi generalizada para a classe de distribuições elípticas. A competência dos laboratórios é avaliada pelo teste da razão de verossimilhança generalizada, do qual, obtemos a distribuição exata para a estatística proposta. Em situações onde os laboratórios medem várias vezes em vários pontos e a variável em análise apresenta variações naturais, utilizou-se o modelo com erro nas variáveis. Diante disso, vamos estender o modelo estrutural definido em Barnett [13] (1969) para o modelo ultra-estrutural com réplicas. Neste caso, vamos avaliar não somente a tendência aditiva, mas também, a tendência multiplicativa, ou seja, avaliar a linearidade das medições. As estimativas dos parâmetros foram obtidas via procedimento do algorítmo EM, com isso, desenvolvemos os teste de Wald, razão de verossimilhança e escore para avaliar a competência dos laboratórios. Nos dois modelos propostos, generalizamos o erro normalizado (En) sugerido pelo Guia 43 [37] para testar a competência dos laboratórios participantes de programas de ensaio de proficiência. Apresentamos também, um procedimento para calcular índices de performance para processos univariados e multivariados. Nestes casos, consideramos que a distribuição dos dados segue uma distribuição Normal assimétrica. Além disso, apresentamos uma análise de simulação onde concluímos que a presença de assimetria nos dados pode causar interpretações erradas sobre o processo, quando a distribuição assumida para os dados é a Normal / We present some applications of statistical tools that are used in the improvement of the quality of industrial processes. Initially, we develop procedures to test the ability of laboratories that participate of programs of proficiency test. In situations where the laboratories measure several times in the same point, we use the model of errors of measurement, considered for Jaech [39](1985). Moreover, the inference on the parameters additive bias was generalized for the class of elliptical distributions. The ability of the laboratories is evaluated by the generalized likelihood ratio test, of which, we get the accurate distribution for the statistics proposal. In situations where the laboratories measure some times in some points and the variable in analysis presents natural variations, uses the model with error in the variable. With this, we go to extend the model structural defined in Barnett [13] (1969) for the ultrastructural model with replicate. In this case, we go to not only evaluate the bias additive, but also, the bias multiplicative, that is, to evaluate the linearity of the measurements. The estimates of the parameters had been gotten by the procedure of the EM algorithm, with this, develop of Wald, likelihood ratio and score test to evaluate the ability of the laboratories. In the two considered models, we generalize the normalized error (En) suggested for Guide 43 [37] to test the ability of the participant laboratories of programs of proficiency test. We also present, a procedure to calculate index of performance for univariate and multivariate processes. In these cases, we consider that the distribution of the data follows a skew Normal distribution. Moreover, we present a simulation analysis where we conclude that the presence of asymmetry in the data can cause interpretations missed on the process, when the distribution assumed for the data is the Normal

Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.

Cette thèse s'intéresse à l'estimation de certaines mesures de risque d'une variable aléatoire réelle Y en présence d'une covariable X. Pour cela, on va considérer que le vecteur (X,Y) suit une loi elliptique. Dans un premier temps, on va s'intéresser aux quantiles de Y sachant X=x. On va alors tester d'abord un modèle de régression quantile assez répandu dans la littérature, pour lequel on obtient des résultats théoriques que l'on discutera. Face aux limites d'un tel modèle, en particulier pour des niveaux de quantile dits extrêmes, on proposera une nouvelle approche plus adaptée. Des résultats asymptotiques sont donnés, appuyés par une étude numérique puis par un exemple sur des données réelles. Dans un second chapitre, on s'intéressera à une autre mesure de risque appelée expectile. La structure du chapitre est sensiblement la même que celle du précédent, à savoir le test d'un modèle de régression inadapté aux expectiles extrêmes, pour lesquels on propose une approche méthodologique puis statistique. De plus, en mettant en évidence le lien entre les quantiles et expectiles extrêmes, on s'aperçoit que d'autres mesures de risque extrêmes sont étroitement liées aux quantiles extrêmes. On se concentrera sur deux familles appelées Lp-quantiles et mesures d'Haezendonck-Goovaerts, pour lesquelles on propose des estimateurs extrêmes. Une étude numérique est également fournie. Enfin, le dernier chapitre propose quelques pistes pour traiter le cas où la taille de la covariable X est grande. En constatant que nos estimateurs définis précédemment étaient moins performants dans ce cas, on s'inspire alors de quelques méthodes d'estimation en grande dimension pour proposer d'autres estimateurs. Une étude numérique permet d'avoir un aperçu de leurs performances / This PhD thesis focuses on the estimation of some risk measures for a real random variable Y with a covariate vector X. For that purpose, we will consider that the random vector (X,Y) is elliptically distributed. In a first time, we will deal with the quantiles of Y given X=x. We thus firstly investigate a quantile regression model, widespread in the litterature, for which we get theoretical results that we discuss. Indeed, such a model has some limitations, especially when the quantile level is said extreme. Therefore, we propose another more adapted approach. Asymptotic results are given, illustrated by a simulation study and a real data example.In a second chapter, we focus on another risk measure called expectile. The structure of the chapter is essentially the same as that of the previous one. Indeed, we first use a regression model that is not adapted to extreme expectiles, for which a methodological and statistical approach is proposed. Furthermore, highlighting the link between extreme quantiles and expectiles, we realize that other extreme risk measures are closely related to extreme quantiles. We will focus on two families called Lp-quantiles and Haezendonck-Goovaerts risk measures, for which we propose extreme estimators. A simulation study is also provided. Finally, the last chapter is devoted to the case where the size of the covariate vector X is tall. By noticing that our previous estimators perform poorly in this case, we rely on some high dimensional estimation methods to propose other estimators. A simulation study gives a visual overview of their performances

Este trabalho apresenta um estudo de inferência e diagnósticos em modelos assimétricos. A análise de influência é baseada na metodologia para modelos com dados incompletos, que é relacionada ao algoritmo EM (Zhu e Lee, 2001). Além dos modelos de regressão Normal Assimétrico (Azzalini, 1999) e t-Normal Assimétrico (Gómez, Venegas e Bolfarine, 2007) existentes, são desenvolvidas duas novas classes de modelos, denominados modelos de misturas de escala normal assimétricos (englobando as distribuições Normal, t-Normal, Slash, Normal-Contaminada e Exponencial-potência Assimétricas) e modelos lineares mistos robustos assimétricos, utilizando distribuições de misturas de escalas normais assimétricas para o efeito aleatório e distribuições de misturas de escalas para o erro aleatório. Para o modelo misto, a matriz de informação de Fisher observada é calculada utilizando a aproximação de Louis (1982) para dados incompletos. Para todos os modelos, algoritmos tipo EM são desenvolvidos de forma a fornecer uma solução numérica para os parâmetros dos modelos de regressão. Para cada modelo de regressão, medidas de bondade de ajuste são realizadas via inspeção visual do gráfico de envelope simulado. Para os modelos de misturas de escalas normais assimétricos, um estudo de robustez do algoritmo EM proposto é desenvolvido, determinando a eficácia dos estimadores apresentados. Aplicações dos modelos estudados são realizadas para os conjuntos de dados do Australian Institute of Sports (AIS), para o conjunto de dados sobre qualidade de vida de pacientes (mulheres) com câncer de mama, em um estudo realizado pelo Centro de Atenção Integral à Saúde da Mulher (CAISM) em conjunto com a Faculdade de Ciências Médicas, da Universidade Estadual de Campinas e para o conjunto de dados de colesterol de Framingham. / This work presents a study of inference and diagnostic in asymmetric models. The influence analysis is based in the methodology for models with incomplete data, that is related to the algorithm EM (Zhu and Lee, 2001). Beyond of the existing asymmetric normal (Azzalini, 1999) and t-Normal asymmetric (Gómez, Venegas and Bolfarine, 2007) regression models, are developed two new classes of models, namely asymmetric normal scale mixture models (embodying the asymmetric Normal, t-Normal, Slash, Contaminated-Normal and Power-Exponential distributions) and asymmetric robust linear mixed models, utilizing asymmetric normal scale mixture distributions for the random effect and normal scale mixture distributions for the random error. For the mixed model, the observed Fisher information matrix is calculated using the Louis\' (1982) approach for incomplete data. For all models, EM algorithms are developed, that provide a numeric solution for the parameters of the regression models. For each regression model, measures of goodness of fit are realized through visual inspection of the graphic of simulated envelope. For the asymmetric normal scale mixture models, a study of robustness of the proposed EM algorithm is developed to determine the efficacy of the presented estimators. Applications of the studied models are made for the data set of the Australian Institute of Sports (AIS), for the data set about quality of life of patients (women) with breast cancer, in a study made by Centro de Atenção Integral à Saúde da Mulher (CAISM) in conjoint with the Medical Sciences Faculty, of the Campinas State\'s University and for the data set of Framingham\'s cholesterol study.

Mελετάται η μίξη δύο συνεχών κατανομών με αύξοντα ρυθμό αποτυχίας και δίνονται συνθήκες για να έχει η μίξη φθίνοντα ρυθμό αποτυχίας. Όταν η μία από τις δύο κατανομές της μίξης είναι η εκθετική γίνεται αντιστροφή του ρυθμού αποτυχίας. Στην περίπτωση της μίξης δύο κανονικών κατανομών παρουσιάζεται ο τρόπος που συνδέεται το πλήθος των κορυφών της πυκνότητας με τον ρυθμό αποτυχίας της μίξης. Mελετάται επίσης, η μονοτονία του ρυθμού αποτυχίας διακριτών κατανομών χρησιμοποιώντας τον λόγο δύο διαδοχικών πιθανοτήτων και δίδεται μία συνθήκη για να έχει η μίξη δύο διακριτών κατανομών φθίνοντα ρυθμό αποτυχίας όταν η μία από τις δύο κατανομές της μίξης είναι η γεωμετρική. Τέλος, χρησιμοποιώντας τον λόγο διαδοχικών πιθανοτήτων, μελετούμε την μονοτονία του ρυθμού αποτυχίας για διδιάστατες διακριτές κατανομές. / The mixture of two continuous distributions, with increasing failure rates, is considered and the necessary conditions to have decreasing failure rate (DFR) are given. When one of these distributions is the Exponential, reversal of the failure rate is observed. In the case of two normal distributions the failure rate is associated with the number of modes. It is also considered the failure rate for discrete distributions in regard with the ratio of two consecutive probabilities. A condition to have DFR is given when one of the distributions of the mixture is the geometric. Finally, we make use of the ratio of two consecutive probabilities to study the failure rate for bivariate discrete distributions.

Disertacijoje nagrinėjamas mokslo publikacijų automatinio klasifikavimo uždavinys. Šis uždavinys sprendžiamas taikant tikimybinius diskriminantinės analizės metodus. Pagrindinis darbo tikslas - sukurti konstruktyvius klasifikavimo metodus, kurie leistų atsižvelgti į mokslo publikacijų tekstų specifiką. Disertaciją sudaro įvadas, trys pagrindiniai skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir vienas priedas. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas pagrindinis darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, pasiektų rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir pranešimai konferencijose bei disertacijos struktūra. Pirmajame skyriuje matematiškai apibrėžtas ir detalizuotas sprendžiamas uždavinys, pateikta analitinė kitų autorių darbų apžvalga. Pasirinkti ir išanalizuoti keli populiarūs klasifikavimo algoritmai, kurie eksperimentinėje darbo dalyje lyginti su autoriaus pasiūlytaisiais. Antrajame skyriuje sudarytas mokslo terminijos pasiskirstymo tekstuose tikimybinis modelis, išskirti atskiri atvejai, galiojant įvestoms prielaidoms apie terminų tarpusavio sąryšių formas, pasiūlytos modelio identifikavimo procedūros bei suformuluoti konstruktyvūs mokslo publikacijų klasifikavimo algoritmai. Trečiajame skyriuje pateikti pagrindiniai... [toliau žr. visą tekstą] / The dissertation considers the problem of automatic classification of scientific publications. The problem is addressed by using probabilistic methods of the discriminant analysis. The main goal of the dissertation is to create constructive classification methods that would allow to take into consideration specificity of scientific publication text. The dissertation consists of Introduction, 3 chapters, Conclusions, References, list of author's publications, and one Appendix. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the paper, research methodology, scientific novelty, the practical significance of results examined in the paper and defended statements. The introduction ends in presenting the author’s publications on the subject of the defended dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1 presents a detailed mathematical formulation of the considered problem, reviews scientific papers on the subject, and analyses a few popular classification algorithms that in Chapter 3 are compared to the ones proposed in this paper. Chapter 2 develops the probabilistic model for scientific terminology distribution over texts, discusses special cases of the model under specific assumptions on forms of terminology relations, suggests the model identification procedures, and formulates constructive scientific... [to full text]

The dissertation considers the problem of automatic classification of scientific publications. The problem is addressed by using probabilistic methods of the discriminant analysis. The main goal of the dissertation is to create constructive classification methods that would allow to take into consideration specificity of scientific publication text. The dissertation consists of Introduction, 3 chapters, Conclusions, References, list of author's publications, and one Appendix. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the paper, research methodology, scientific novelty, the practical significance of results examined in the paper and defended statements. The introduction ends in presenting the author’s publications on the subject of the defended dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1 presents a detailed mathematical formulation of the considered problem, reviews scientific papers on the subject, and analyses a few popular classification algorithms that in Chapter 3 are compared to the ones proposed in this paper. Chapter 2 develops the probabilistic model for scientific terminology distribution over texts, discusses special cases of the model under specific assumptions on forms of terminology relations, suggests the model identification procedures, and formulates constructive scientific... [to full text] / Disertacijoje nagrinėjamas mokslo publikacijų automatinio klasifikavimo uždavinys. Šis uždavinys sprendžiamas taikant tikimybinius diskriminantinės analizės metodus. Pagrindinis darbo tikslas - sukurti konstruktyvius klasifikavimo metodus, kurie leistų atsižvelgti į mokslo publikacijų tekstų specifiką. Disertaciją sudaro įvadas, trys pagrindiniai skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir vienas priedas. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas pagrindinis darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, pasiektų rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir pranešimai konferencijose bei disertacijos struktūra. Pirmajame skyriuje matematiškai apibrėžtas ir detalizuotas sprendžiamas uždavinys, pateikta analitinė kitų autorių darbų apžvalga. Pasirinkti ir išanalizuoti keli populiarūs klasifikavimo algoritmai, kurie eksperimentinėje darbo dalyje lyginti su autoriaus pasiūlytaisiais. Antrajame skyriuje sudarytas mokslo terminijos pasiskirstymo tekstuose tikimybinis modelis, išskirti atskiri atvejai, galiojant įvestoms prielaidoms apie terminų tarpusavio sąryšių formas, pasiūlytos modelio identifikavimo procedūros bei suformuluoti konstruktyvūs mokslo publikacijų klasifikavimo algoritmai. Trečiajame skyriuje pateikti pagrindiniai... [toliau žr. visą tekstą]

Les temps de réponse dans une tache de reconnaissance d’objets visuels diminuent de façon significative lorsque les cibles peuvent être distinguées à partir de deux attributs redondants. Le gain de redondance pour deux attributs est un résultat commun dans la littérature, mais un gain causé par trois attributs redondants n’a été observé que lorsque ces trois attributs venaient de trois modalités différentes (tactile, auditive et visuelle). La présente étude démontre que le gain de redondance pour trois attributs de la même modalité est effectivement possible. Elle inclut aussi une investigation plus détaillée des caractéristiques du gain de redondance. Celles-ci incluent, outre la diminution des temps de réponse, une diminution des temps de réponses minimaux particulièrement et une augmentation de la symétrie de la distribution des temps de réponse. Cette étude présente des indices que ni les modèles de course, ni les modèles de coactivation ne sont en mesure d’expliquer l’ensemble des caractéristiques du gain de redondance. Dans ce contexte, nous introduisons une nouvelle méthode pour évaluer le triple gain de redondance basée sur la performance des cibles doublement redondantes. Le modèle de cascade est présenté afin d’expliquer les résultats de cette étude. Ce modèle comporte plusieurs voies de traitement qui sont déclenchées par une cascade d’activations avant de satisfaire un seul critère de décision. Il offre une approche homogène aux recherches antérieures sur le gain de redondance. L’analyse des caractéristiques des distributions de temps de réponse, soit leur moyenne, leur symétrie, leur décalage ou leur étendue, est un outil essentiel pour cette étude. Il était important de trouver un test statistique capable de refléter les différences au niveau de toutes ces caractéristiques. Nous abordons la problématique d’analyser les temps de réponse sans perte d’information, ainsi que l’insuffisance des méthodes d’analyse communes dans ce contexte, comme grouper les temps de réponses de plusieurs participants (e. g. Vincentizing). Les tests de distributions, le plus connu étant le test de Kolmogorov- Smirnoff, constituent une meilleure alternative pour comparer des distributions, celles des temps de réponse en particulier. Un test encore inconnu en psychologie est introduit : le test d’Anderson-Darling à deux échantillons. Les deux tests sont comparés, et puis nous présentons des indices concluants démontrant la puissance du test d’Anderson-Darling : en comparant des distributions qui varient seulement au niveau de (1) leur décalage, (2) leur étendue, (3) leur symétrie, ou (4) leurs extrémités, nous pouvons affirmer que le test d’Anderson-Darling reconnait mieux les différences. De plus, le test d’Anderson-Darling a un taux d’erreur de type I qui correspond exactement à l’alpha tandis que le test de Kolmogorov-Smirnoff est trop conservateur. En conséquence, le test d’Anderson-Darling nécessite moins de données pour atteindre une puissance statistique suffisante. / Response times in a visual object recognition task decrease significantly if targets can be distinguished by two redundant attributes. Redundancy gain for two attributes is a common finding, but redundancy gain from three attributes has been found only for stimuli from three different modalities (tactile, auditory, and visual). This study extends those results by showing that redundancy gain from three attributes within the visual modality is possible. It also provides a more detailed investigation of the characteristics of redundancy gain. Apart from a decrease in response times for redundant targets, these include a decrease in minimal response times and an increase in symmetry of the response time distribution. This study further presents evidence that neither race models nor coactivation models can account for all characteristics of redundancy gain. In this context, we discuss the problem of calculating an upper limit for the performance of race models for triple redundant targets, and introduce a new method of evaluating triple redundancy gain based on performance for double redundant targets. In order to explain the results from this study, the cascade race model is introduced. The cascade race model consists of several input channels, which are triggered by a cascade of activations before satisfying a single decision criterion, and is able to provide a unifying approach to previous research on the causes of redundancy gain. The analysis of the characteristics of response time distributions, including their mean, symmetry, onset, and scale, is an essential tool in this study. It was therefore important to find an adequate statistical test capable of reflecting differences in all these characteristics. We discuss the problem and importance of analysing response times without data loss, as well as the inadequacy of common methods of analysis such as the pooling of response times across participants (e.g. Vincentizing) in the present context. We present tests of distributions as an alternative method for comparing distributions, response time distributions in particular, the most common of these being the Kolmogorov-Smirnoff test. We also introduce a test yet unknown in psychology: the two-sample Anderson-Darling test of goodness of fit. We compare both tests, presenting conclusive evidence that the Anderson-Darling test is more accurate and powerful: when comparing two distributions that vary (1) in onset only, (2) in scale only, (3) in symmetry only, or (4) that have the same mean and standard deviation but differ on the tail ends only, the Anderson-Darling test proves to detect differences better than the Kolmogorov-Smirnoff test. Finally, the Anderson-Darling test has a type I error rate corresponding to alpha whereas the Kolmogorov-Smirnoff test is overly conservative. Consequently, the Anderson- Darling test requires less data than the Kolmogorov-Smirnoff test to reach sufficient statistical power.

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-08-03T20:04:27Z No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:23Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:30Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Made available in DSpace on 2017-08-08T19:15:39Z (GMT). No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) Previous issue date: 2016-12-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.