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Master's Dissertation
DOI
10.11606/D.55.2018.tde-15032018-084127
Document
Author
Full name
Gilberto de Araujo Pereira
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1998
Supervisor
Committee
Achcar, Jorge Alberto (President)
Bolfarine, Heleno
Oishi, Jorge
Title in Portuguese
Modelos de Mistura para Dados de Sobrevivência na Presença de Covariáveis, Utilizando Métodos Bayesianos
Keywords in Portuguese
Não disponível
Abstract in Portuguese
Nesta dissertação, desenvolvemos uma análise Bayesiana de modelos de mistura finita de distribuições, para dados de sobrevivência sem censura, com censura tipo II e dados censurados por intervalos, na presença de uma covariável. Consideramos os algoritmos amostrador de Gibbs com Metropolis-Hastings, e utilizamos os estimadores de Monte Carlo para conseguir as quantitades à posteriori de interesse, assumindo diferentes escolhas para as (J = 2) densidades no modelo de mistura, como por exemplo a mistura de, duas distribuições potência exponencial a qual considera uma grande classe de distribuições simétricas, duas distribuições normais, normal-exponencial e gamma-normal. Apresentamos também ah gumas considerações na seleção do modelo utilizando as densidades preditivas (CP0)preditivas condicionais ordenadas e introduzimos três exemplos numéricos para ilustrar a metodologia proposta.
Title in English
Not available
Keywords in English
Not available
Abstract in English
In this dissertation, we present a Bayesian analysis of distributions finite mixture models, for survival data uncensored, type II censoring and interval-censored data, In the presence of one covaziate. Considering Gibbs sampling with Metropolis-Hastings algorithms, we get Monte Cano estimates for the posterior quantities of interest, assuming different choices for the (J = 2) densities in the mixture model, for example a mixture, two exponential power distributions which includes a wider class of symmetric distributions, two normal distributions, normal-exponential and gammanormal distributions. We also present some considerations on model selection, considering the predictive densities (CPO) conditional predictive ordinate, and we introduce three numerical example to illustrate the proposed methodology.
 
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Publishing Date
2018-03-15
 
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