We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

This research aims to integrate mathematical water quality models with Bayesian inference techniques for obtaining effective model calibration and rigorous assessment of the uncertainty underlying model predictions. The first part of my work combines a Bayesian calibration framework with a complex biogeochemical model to reproduce oligo-, meso- and eutrophic lake conditions. The model accurately describes the observed patterns and also provides realistic estimates of predictive uncertainty for water quality variables. The Bayesian estimations are also used for appraising the exceedance frequency and confidence of compliance of different water quality criteria. The second part introduces a Bayesian hierarchical framework (BHF) for calibrating eutrophication models at multiple systems (or sites of the same system). The models calibrated under the BHF provided accurate system representations for all the scenarios examined. The BHF allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites. Both frameworks can facilitate environmental management decisions.

Calif., Univ. of California, Diss.--Irvine, 2003. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich. - Enth. 3 Beitr.

Originally presented as the author's Thesis (Ph. D.)--WHU, Otto-Beisheim School of Management, Vallendar, Germany. / Includes bibliographical references (p. 195-205) and index.

Zugl.: Frankfurt (Main), Univ., Diss., 2009

La thèse est divisée en deux parties portant sur deux aspects relativement différents des approches bayésiennes non-paramétriques. Dans la première partie, nous nous intéressons aux propriétés fréquentistes (asymptotiques) de lois a posteriori pour des paramètres appartenant à l'ensemble des suites réelles de carré sommable. Dans la deuxième partie, nous nous intéressons à des approches non-paramétriques modélisant des données d'espèces et leur diversité en fonction de certaines variables explicatives, à partir de modèles qui utilisent des mesures de probabilité aléatoires. / This thesis is divided in two parts on rather different aspects of Bayesian statistics. In the first part, we deal with frequentist (asymptotic) properties of posterior distributions for parameters which belong to the space of real square sommable sequences. In the second part, we deal with nonparametric approaches modelling species data and the diversity of these data with respect to covariates. To that purpose, we use models based on random probability measures.

Submitted by Raniere Barreto (raniere.barros@ufvjm.edu.br) on 2018-04-13T17:44:56Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) wallinson_oliveira_schutte.pdf: 2955879 bytes, checksum: 7d21ec1707fa5cf82b0b96642d27fa03 (MD5) / Rejected by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br), reason: Verificar refer?ncia e keywords. on 2018-04-20T15:00:26Z (GMT) / Submitted by Raniere Barreto (raniere.barros@ufvjm.edu.br) on 2018-05-15T18:16:15Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) wallinson_oliveira_schutte.pdf: 2955879 bytes, checksum: 7d21ec1707fa5cf82b0b96642d27fa03 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-05-15T19:38:43Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) wallinson_oliveira_schutte.pdf: 2955879 bytes, checksum: 7d21ec1707fa5cf82b0b96642d27fa03 (MD5) / Made available in DSpace on 2018-05-15T19:38:43Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) wallinson_oliveira_schutte.pdf: 2955879 bytes, checksum: 7d21ec1707fa5cf82b0b96642d27fa03 (MD5) Previous issue date: 2017 / O presente estudo prop?e o desenvolvimento de duas ferramentas para se fazer a classifica??o de batimentos card?acos a fim de detectar a Isquemia Card?aca. Uma baseada em propriedades da Distribui??o Normal e Teorema de Bayes e a outra baseada em Redes Neurais Artificiais. Utilizando o banco de dados Long-Term ST Database, foi efetuado um filtro de dados, que foram agrupados pelas seguintes deriva??es: A-S, E-S, A-I, ML2, MV2, ML3, V4 e V5. Por meio dos algoritmos propostos, implementados por interm?dio da Linguagem de Programa??o PHP, p?de-se verificar a deriva??o mais prop?cia a se detectar essa doen?a. Foi poss?vel observar as deriva??es V5 e A-S com melhores resultados utilizando-se o algoritmo h?brido. Na V5, foi obtido Sensibilidade de 100%, Especificidade de 97%, Valor Preditivo Positivo de 95.89% e Valor Preditivo Negativo de 100% e, na A-S, valores de 99.22%, 99.99%, 99.99% e 99.61% para Sensibilidade, Especificidade, Valor Preditivo Positivo e Valor Preditivo Negativo. O algoritmo de Redes Neurais Artificiais apresentou o melhor resultado para deriva??o A-S com 99.98%, 100%, 100% e 99.99% para Sensibilidade, Especificidade, Valor Preditivo Positivo e Valor Preditivo Negativo respectivamente. Tamb?m foi calculado o intervalo de confian?a para propor??es populacionais com 95% de confian?a, a fim de se estabelecer n?veis de precis?o das bases utilizadas. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Tecnologia, Sa?de e Sociedade, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / The present study proposes the development of two tools to classify heart beats in order to detect cardiac ischemia. One based on properties of Normal Distribution and Bayes' Theorem and the other based on Artificial Neural Networks. Using the Long-Term ST Database, a data filter was performed, which was grouped by the following derivations: A-S, E-S, A-I, ML2, MV2, ML3 and V4, V5. By means of the algorithms proposed, implemented through the PHP Programming Language, we could verify the most favorable derivation to detect this disease. It was possible to observe the V5 and A-S leads with better results using the hybrid algorithm. In V5, 100% sensitivity, 97% specificity, 95.89% positive predictive value and 100% negative predictive value were obtained, and in A-S, values of 99.22%, 99.99%, 99.99% and 99.61% for sensitivity, specificity, positive predictive value, and negative predictive value. The algorithm of Artificial Neural Networks presented the best result for A-S derivation with 99.98%, 100%, 100% and 99.99% for sensitivity, specificity, positive predictive value and negative predictive value respectively. We also calculated the confidence interval for population proportions with 95% confidence in order to establish precision levels of the bases used. / El presente estudio propone el desarrollo de dos herramientas para la clasificaci?n de los latidos del coraz?n con el fin de detectar la isquemia card?aca. Uno basado en las propiedades de la Distribuci?n Normal y el Teorema de Bayes y el otro basado en las Redes Neuronales Artificiales. Utilizando la base de datos Long-Term ST Database, se llev? a cabo un filtro de datos, que fueron agrupados seg?n las siguientes derivaciones: a-S, E, S, A-I, ML2, MV2, ML3, V4 y V5. Por medio de los algoritmos propuestos, implementado a trav?s del lenguaje de programaci?n PHP, pudimos comprobar la derivaci?n m?s favorable para detectar esta enfermedad. Fue posible observar las derivaciones V5 y A-S con mejores resultados utilizando el algoritmo h?brido. En V5 se obtuvo una sensibilidad del 100%, uma especificidad del 97%, 95.89% de valor predictivo positivo y 100% de valor predictivo negativo y en A-S, valores de 99.22%, 99.99%, 99.99% y 99.61% para la sensibilidad, especificidad, valor predictivo positivo, y valor predictivo negativo respectivamente. El algoritmo de Redes Neuronales Artificiales present? el mejor resultado para la derivaci?n A-S con 99.98%, 100%, 100% y 99.99% para la sensibilidad, especificidad, valor predictivo positivo y valor predictivo negativo, respectivamente. Tambi?n fue calculado el intervalo de confianza para proporciones de las poblaciones con 95% de confianza con el fin de establecer los niveles de confianza precisi?n de las bases utilizadas

Savoir comment les patients choisissent les hôpitaux est d'une importance majeure non seulement pour les gestionnaires des hôpitaux mais aussi pour les décideurs. Il s'agit entre autres pour les premiers, de la gestion des flux et l'offre des soins et pour les seconds, l'implémentation des reformes dans le système de santé.Nous proposons dans cette thèse différentes modélisations des données d'admission de patients en fonction de la distance par rapport à un hôpital afin de prévoir le flux des patients et de comparer son attractivité par rapport à d'autres hôpitaux. Par exemple, nous avons utilisé des modèles bayésiens hiérarchiques pour des données de comptage avec possible dépendance spatiale. Des applications on été faites sur des données d'admission de patients dans la région de Languedoc-Roussillon.Nous avons aussi utilisé des modèles de choix discrets tels que les RUMs. Mais vu certaines limites qu'ils présentent pour notre objectif, nous avons relâché l'hypothèse de maximisation d'utilité pour une plus souple et selon laquelle un agent (patient) peut choisir un produit (donc hôpital) dès lors que l'utilité que lui procure ce produit a atteint un certain seuil de satisfaction, en considérant certains aspects. Une illustration de cette approche est faite sur trois hôpitaux de l'Hérault pour les séjours dus à l'asthme en 2009 pour calculer l'envergure territorial d'un hôpital donné . / Understanding how patients choose hospitals is of utmost importance for both hospitals administrators and healthcare decision makers; the formers for patients incoming tide and the laters for regulations.In this thesis, we present different methods of modelling patients admission data in order to forecast patients incoming tide and compare hospitals attractiveness.The two first method use counting data models with possible spatial dependancy. Illustration is done on patients admission data in Languedoc-Roussillon.The third method uses discrete choice models (RUMs). Due to some limitations of these models according to our goal, we introduce a new approach where we released the assumption of utility maximization for an utility-threshold ; that is to say that an agent (patient) can choose an alternative (hospital) since he thinks that he can obtain a certain level of satisfaction of doing so, according to some aspects. Illustration of the approach is done on 2009 asthma admission data in Hérault.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.