In this thesis, we study two fundamental problems that arise in the modeling of stochastic systems: (i) Validation of stochastic models against behavioral specifications such as temporal logics, and (ii) Discovery of kinetic parameters of stochastic biochemical models from behavioral specifications. We present a new Bayesian algorithm for Statistical Model Checking of stochastic systems based on a sequential version of Jeffreys’ Bayes Factor test. We argue that the Bayesian approach is more suited for application do- mains like systems biology modeling, where distributions on nuisance parameters and priors may be known. We prove that our Bayesian Statistical Model Checking algorithm terminates for a large subclass of prior probabilities. We also characterize the Type I/II errors associated with our algorithm. We experimentally demonstrate that this algorithm is suitable for the analysis of complex biochemical models like those written in the BioNetGen language. We then argue that i.i.d. sampling based Statistical Model Checking algorithms are not an effective way to study rare behaviors of stochastic models and present another Bayesian Statistical Model Checking algorithm that can incorporate non-i.i.d. sampling strategies. We also present algorithms for synthesis of chemical kinetic parameters of stochastic biochemical models from high level behavioral specifications. We consider the setting where a modeler knows facts that must hold on the stochastic model but is not confident about some of the kinetic parameters in her model. We suggest algorithms for discovering these kinetic parameters from facts stated in appropriate formal probabilistic specification languages. Our algorithms are based on our theoretical results characterizing the probability of a specification being true on a stochastic biochemical model. We have applied this algorithm to discover kinetic parameters for biochemical models with as many as six unknown parameters.

Text in Afrikaans / Weens die veeleisende aard van sorggewing aan VIGS-weeskinders, bevind die sorggewers hulle dikwels in 'n posisie waar hulle self sorg en ondersteuning nodig het. Die vraag het begin ontstaan op watter manier hierdie sorggewers ondersteun kan word. Dit het duidelik geword dat die kerk vanuit hul sosiale verantwoordelikheid sorg en ondersteuning aan die sorggewers kan bied. Sorggewers van een instansie wat aan die navorsingsreis deelgeneem het, het inderdaad nie genoeg sorg en ondersteuning van die kerk ontvang nie. Hierdie gebrek aan ondersteuning het 'n direkte invloed op die sorggewers se hantering van sorggewingseise. Sorggewers van die ander twee deelnemende instansies ontvang genoeg ondersteuning van lidmate, en dit maak 'n groot verskil aan hoe sorggewingspanning beleef word. In hierdie studie is daar krities gekyk na wyses waarop die kerk betrokke is en verder kan betrokke raak by die sorggewers van VIGSweeskinders. / Philosophy, Practical and Systematic Theology / M.Th. (Praktiese Teologie)

La classification non supervisée consiste à regrouper des objets afin de former des groupes homogènes au sens d’une mesure de similitude. C’est un outil utile pour explorer la structure d’un ensemble de données non étiquetées. Par ailleurs, les méthodes à noyau, introduites initialement dans le cadre supervisé, ont démontré leur intérêt par leur capacité à réaliser des traitements non linéaires des données en limitant la complexité algorithmique. En effet, elles permettent de transformer un problème non linéaire en un problème linéaire dans un espace de plus grande dimension. Dans ce travail, nous proposons un algorithme de classification hiérarchique ascendante utilisant le formalisme des méthodes à noyau. Nous avons tout d’abord recherché des mesures de similitude entre des distributions de probabilité aisément calculables à l’aide de noyaux. Parmi celles-ci, la maximum mean discrepancy a retenu notre attention. Afin de pallier les limites inhérentes à son usage, nous avons proposé une modification qui conduit au critère de Ward, bien connu en classification hiérarchique. Nous avons enfin proposé un algorithme itératif de clustering reposant sur la classification hiérarchique à noyau et permettant d’optimiser le noyau et de déterminer le nombre de classes en présence / Clustering, as a useful tool for unsupervised classification, is the task of grouping objects according to some measured or perceived characteristics of them and it has owned great success in exploring the hidden structure of unlabeled data sets. Kernel-based clustering algorithms have shown great prominence. They provide competitive performance compared with conventional methods owing to their ability of transforming nonlinear problem into linear ones in a higher dimensional feature space. In this work, we propose a Kernel-based Hierarchical Agglomerative Clustering algorithms (KHAC) using Ward’s criterion. Our method is induced by a recently arisen criterion called Maximum Mean Discrepancy (MMD). This criterion has firstly been proposed to measure difference between different distributions and can easily be embedded into a RKHS. Close relationships have been proved between MMD and Ward's criterion. In our KHAC method, selection of the kernel parameter and determination of the number of clusters have been studied, which provide satisfactory performance. Finally an iterative KHAC algorithm is proposed which aims at determining the optimal kernel parameter, giving a meaningful number of clusters and partitioning the data set automatically

Orientador: Filidor Edilfonso Vilca Labra / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-24T16:29:00Z (GMT). No. of bitstreams: 1 Romeiro_RenataGuimaraes_M.pdf: 10761224 bytes, checksum: 3606332b6846c959d076e318f1667133 (MD5) Previous issue date: 2014 / Resumo: O modelo de regressão Birnbaum-Saunders de Rieck e Nedelman (1991) tem sido amplamente discutido por vários autores, com aplicações na área de sobrevivência e confiabilidade. Neste trabalho, desenvolvemos um modelo de regressão Birnbaum-Saunders bivariado através do uso da distribuição Senh-Normal proposta por Rieck (1989). Este modelo de regressão pode ser utilizado para analisar logaritmos de tempos de vida de duas unidades correlacionadas, e gera marginais correspondentes aos modelos de regressão Birnbaum-Saunders univariados. Apresentamos um estudo de inferência e análise de diagnóstico para modelo de regressão Birnbaum-Saunders bivariado proposto. Em primeiro lugar, apresentamos os estimadores obtidos através do método dos momentos e de máxima verossimilhança, e a matriz de informação observada de Fisher. Além disso, discutimos testes de hipóteses com base na normalidade assintótica dos estimadores de máxima verossimilhança. Em segundo lugar, desenvolvemos um método de diagnóstico para o modelo de regressão Birnbaum- Saunders bivariado baseado na metodologia de Cook (1986). Finalmente, apresentamos alguns resultados de estudos de simulações e aplicações em dados reais / Abstract: The Birnbaum-Saunders regression model of Rieck and Nedelman (1991) has been extensively discussed by various authors with application in survival and reliability studies. In this work a bivariate Birnbaum-Saunders regression model is developed through the use of Sinh-Normal distribution proposed by Rieck (1989). This bivariate regression model can be used to analyze correlated log-time of two units, it bivariate regression model has its marginal as the Birnbaum- Saunders regression model. For the bivariate Birnbaum-Saunders regression model is discussed some of its properties, in the moment estimation, the maximum likelihood estimation and the observed Fisher information matrix. Hypothesis testing is performed by using the asymptotic normality of the maximum-likelihood estimators. Influence diagnostic methods are developed for this model based on the Cook¿s(1986) approach. Finally, the results of a simulation study as well as an application to a real data set are presented / Mestrado / Estatistica / Mestra em Estatística

Orientador: Ronaldo Dias / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-25T00:25:46Z (GMT). No. of bitstreams: 1 Morellato_SauloAlmeida_D.pdf: 32854783 bytes, checksum: 040664acd0c8f1efe07cedccda8d11f6 (MD5) Previous issue date: 2014 / Resumo: Este trabalho aborda dois problemas de inferência relacionados à regressão múltipla não paramétrica: a estimação em modelos aditivos usando um método não paramétrico e o teste de hipóteses para igualdade de curvas ajustadas a partir do modelo. Na etapa de estimação é construída uma generalização dos métodos h-splines, tanto no contexto sequencial adaptativo proposto por Dias (1999), quanto no contexto bayesiano proposto por Dias e Gamerman (2002). Os métodos h-splines fornecem uma escolha automática do número de bases utilizada na estimação do modelo. Estudos de simulação mostram que os resultados obtidos pelos métodos de estimação propostos são superiores aos conseguidos nos pacotes gamlss, mgcv e DPpackage em R. São criados dois testes de hipóteses para testar H0 : f = f0. Um teste de hipóteses que tem sua regra de decisão baseada na distância quadrática integrada entre duas curvas, referente à abordagem sequencial adaptativa, e outro baseado na medida de evidência bayesiana proposta por Pereira e Stern (1999). No teste de hipóteses bayesiano o desempenho da medida de evidência é observado em vários cenários de simulação. A medida proposta apresentou um comportamento que condiz com uma medida de evidência favorável à hipótese H0. No teste baseado na distância entre curvas, o poder do teste foi estimado em diversos cenários usando simulações e os resultados são satisfatórios. Os procedimentos propostos de estimação e teste de hipóteses são aplicados a um conjunto de dados referente ao trabalho de Tanaka e Nishii (2009) sobre o desmatamento no leste da Ásia. O objetivo é escolher um entre oito modelos candidatos. Os testes concordaram apontando um par de modelos como sendo os mais adequados / Abstract: In this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitableIn this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitable / Doutorado / Estatistica / Doutor em Estatística

Le XXIème siècle étant le siècle du passage au tout numérique, les médias digitaux jouent un rôle de plus en plus important. Les logiciels sophistiqués de retouche d’images se sont démocratisés et permettent de diffuser facilement des images falsifiées. Ceci pose un problème sociétal puisqu’il s’agit de savoir si ce que l’on voit a été manipulé. Cette thèse s'inscrit dans le cadre de la criminalistique des images. Trois problèmes sont abordés : l'identification de l'origine d'une image, la détection d'informations cachées dans une image et la détection d'un exemple falsification : le rééchantillonnage. Ces travaux s'inscrivent dans le cadre de la théorie de la décision statistique et proposent la construction de détecteurs permettant de respecter une contrainte sur la probabilité de fausse alarme. Afin d'atteindre une performance de détection élevée, il est proposé d'exploiter les propriétés des images naturelles en modélisant les principales étapes de la chaîne d'acquisition d'un appareil photographique. La méthodologie, tout au long de ce manuscrit, consiste à étudier le détecteur optimal donné par le test du rapport de vraisemblance dans le contexte idéal où tous les paramètres du modèle sont connus. Lorsque des paramètres du modèle sont inconnus, ces derniers sont estimés afin de construire le test du rapport de vraisemblance généralisé dont les performances statistiques sont analytiquement établies. De nombreuses expérimentations sur des images simulées et réelles permettent de souligner la pertinence de l'approche proposée / The remarkable evolution of information technologies and digital imaging technology in the past decades allow digital images to be ubiquitous. The tampering of these images has become an unavoidable reality, especially in the field of cybercrime. The credibility and trustworthiness of digital images have been eroded, resulting in important consequences in terms of political, economic, and social issues. To restore the trust to digital images, the field of digital forensics was born. Three important problems are addressed in this thesis: image origin identification, detection of hidden information in a digital image and an example of tampering image detection : the resampling. The goal is to develop a statistical decision approach as reliable as possible that allows to guarantee a prescribed false alarm probability. To this end, the approach involves designing a statistical test within the framework of hypothesis testing theory based on a parametric model that characterizes physical and statistical properties of natural images. This model is developed by studying the image processing pipeline of a digital camera. As part of this work, the difficulty of the presence of unknown parameters is addressed using statistical estimation, making the application of statistical tests straightforward in practice. Numerical experiments on simulated and real images have highlighted the relevance of the proposed approach

Le problème de détection et localisation d’anomalie peut être traité comme le problème du test entre des hypothèses multiples (THM) dans le cadre bayésien. Le test bayésien avec la fonction de perte 0−1 est une solution standard pour ce problème, mais les hypothèses alternatives pourraient avoir une importance tout à fait différente en pratique. La fonction de perte 0−1 ne reflète pas cette réalité tandis que la fonction de perte quadratique est plus appropriée. L’objectif de cette thèse est la conception d’un test bayésien avec la fonction de perte quadratique ainsi que son étude asymptotique. La construction de ce test est effectuée en deux étapes. Dans la première étape, un test bayésien avec la fonction de perte quadratique pour le problème du THM sans l’hypothèse de base est conçu et les bornes inférieures et supérieures des probabilités de classification erronée sont calculées. La deuxième étape construit un test bayésien pour le problème du THM avec l’hypothèse de base. Les bornes inférieures et supérieures des probabilités de fausse alarme, des probabilités de détection manquée, et des probabilités de classification erronée sont calculées. A partir de ces bornes, l’équivalence asymptotique entre le test proposé et le test standard avec la fonction de perte 0−1 est étudiée. Beaucoup d’expériences de simulation et une expérimentation acoustique ont illustré l’efficacité du nouveau test statistique / The anomaly detection and localization problem can be treated as a multiple hypotheses testing (MHT) problem in the Bayesian framework. The Bayesian test with the 0−1 loss function is a standard solution for this problem, but the alternative hypotheses have quite different importance in practice. The 0−1 loss function does not reflect this fact while the quadratic loss function is more appropriate. The objective of the thesis is the design of a Bayesian test with the quadratic loss function and its asymptotic study. The construction of the test is made in two steps. In the first step, a Bayesian test with the quadratic loss function for the MHT problem without the null hypothesis is designed and the lower and upper bounds of the misclassification probabilities are calculated. The second step constructs a Bayesian test for the MHT problem with the null hypothesis. The lower and upper bounds of the false alarm probabilities, the missed detection probabilities as well as the misclassification probabilities are calculated. From these bounds, the asymptotic equivalence between the proposed test and the standard one with the 0-1 loss function is studied. A lot of simulation and an acoustic experiment have illustrated the effectiveness of the new statistical test

Classical parametric methods of statistical inference and hypothesis testing are derived under fundamental theoretical assumptions, which may or may not be met in real world applications. However, these methods are usually used despite the violation of their underlying assumptions, while it is argued, that these methods are quite insensitive to the violation of relevant assumptions. Moreover, alternative nonparametric or rank tests are often overlooked, mostly because these methods may be deemed to be less powerful then parametric methods. The aim of the dissertation is therefore a description of the consequences of assumption violations concerning classical one-sample and two-sample statistical methods and a consistent and comprehensive comparison of parametric, nonparametric and robust statistical techniques, which is based on extensive simulation study and focused mostly on a normality and heteroscedasticity assumption violation. The results of the simulation study confirmed that the classical parametric methods are relatively robust, with some reservations in case of outlying observations, when traditional methods may fail. On the other hand, the empirical study clearly proved that the classical parametric methods are losing their optimal properties, when the underlying assumptions are violated. For example, in many cases of non-normality the appropriate nonparametric and rank-based methods are more powerful, and therefore a statement, that these methods are unproductive due to their lack of power may be considered a crucial mistake. However, the choice of the most appropriate distribution-free method generally depends on the particular form of the underlying distribution.

The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 has lead to reduced welfare assistance to the needy. This thesis analyzes how families have become increasingly homeless and hungry during the welfare reform years.

The master’s thesis analyzes the key performance areas of the call centre, which contact debtors of nonbank company, which provides loans. An author analyze a collection process with a wide range of statistical methods and then propose actions, which would lead to do this process more effective, which overall will lead to a better performance of whole call centre.