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Agrupamento de interações não lineares em análise fatorial
(Universidade Federal de Minas GeraisBrasilICX - DEPARTAMENTO DE ESTATÍSTICAPrograma de Pós-Graduação em EstatísticaUFMG, 2020-02-19)
Factor analysis is a powerful tool for dimension reduction in a multivariate statistical study. This Thesis is dedicated to extend the factor model with non-linear interactions proposed in 2013. The main contribution of ...
Inferência em modelos de mistura via algoritmo EM estocástico modificado
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2017-06-02)
We present the topics and theory of Mixture Models in a context of maximum likelihood and Bayesian inferece. We approach clustering methods in both contexts, with emphasis on the stochastic EM algorithm and the Dirichlet ...
Efficient bayesian methods for mixture models with genetic applications
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2016-12-14)
We propose Bayesian methods for selecting and estimating di erent types of mixture models which are widely used in Genetics and Molecular Biology. We speci cally propose data-driven selection and estimation methods for a ...
Bayesian nonparametric hypothesis testingTests de Hipótesis Bayesianos no Paramétricos
(2019)
En esta tesis, se propone un nuevo procedimiento Bayesiano no-paramétrico para prueba de hipótesis cuando los datos están correlacionados. Inicialmente, se presenta una propuesta para comparar distribuciones en muestras ...
Theory and applicatións of dependent nonparametric bayesian models for bounded and unbounded responses
(2012)
The definition and study of theoretical properties of probability models defined on infinite - dimensional spaces have received increasing attention in the statisticalliterature because these models are the basis for the ...
Optimal sampling for repeated binary measurements
(CANADIAN JOURNAL STATISTICS, 2004)
The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling ...
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
(JOURNAL STATISTICAL SOFTWARE, 2011)
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished ...
Bayesian Nonparametric Approaches for ROC Curve Inference
(2015)
The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy ...