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Master's Dissertation
DOI
10.11606/D.55.2018.tde-20032018-090733
Document
Author
Full name
Karin Storani
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1997
Supervisor
Committee
Achcar, Jorge Alberto (President)
Bolfarine, Heleno
Wada, Cicilia Yuko
Title in Portuguese
Uso de Modelos de Estratégia de Tipo II em Confiabilidade de Software
Keywords in Portuguese
Não disponível
Abstract in Portuguese
Nesta dissertação de mesûado, exploramos os modelos estatísticos de confiabilidade de softrvare que utilizam os processos de Poisson homogêneo e não homogêneo para modelagem dos dados de falhas. Para modelar os instantes de falhas, escolhemos a classe de modelos de estatísticas de ordem proposta por Yang (1994). Propomos uma extensão desses modelos, considerando a distribuição gaussiana inversa, para modelar a função de valor médio dos processos de Poisson não homogêneo. Os métodos considerados para fazet inferências pam os parâmetros de interesse são os métodos Bayesianos. Exploramos, ainda, o uso de algorinnos de Metropolis com etapas Gibbs para desenvolver a inferência Bayesiana. Tendo em vista a verificação das suposições dos modelos de estatísticas de ordem, desenvolvemos e incorporamos algumas técnicas Bayesianas de diagnóstico. Baseamos a seleção de modelos nos valores de predição ordenados. A metodologia desenvolvida neste trabalho é exemplificada com conjuntos de dados introduzidos por Jelinski e Moranda (1972) e Goel (1985).
Title in English
Not available
Keywords in English
Not available
Abstract in English
In this dissertation, we explore software reliabilþ models based on homogeneous and nonhomogeneous Poisson process to model softrvare failure data. To model the epochs of the software failures, we choose the family of order statistics models introduced by yang (1994). we propose some generalization of these models considering the Gaussian inverse distribution, to model the mean value function of the nonhomogeneous Poisson process. The proposed methods to get inferences on the pararneters of interest are given by the Bayesian approach. V/e also explore the use of Metropolis-with-Gibbs algorithms to obtain the posterior summaries of interest. To veriff the adequability of the proposed models, we develop some diagnostic Bayesian methods. We choose the best model, based on the values of ordinated predictives. The methodolory presented in this work is illusfiated with the data sets introduced by Jelinski and Moranda (1972) andGoel (19g5).
 
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KarianStorani.pdf (116.10 Mbytes)
Publishing Date
2018-03-20
 
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