Família Weibull de razão de chances na presença de covariáveis

• Ano de defesa: 2009
• Autor(a) principal: Gomes, André Yoshizumi
• Orientador(a): Louzada Neto, Francisco
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Estatística - PPGEs
• País: BR
• Palavras-chave em Português: Estatística; Distribuição Weibull; Razão de chances; Estimador de máxima verossimilhança; Bootstrap (Estatística); Cadeias de Markov; Teoria assintótica; Probabilidade de cobertura; Inferência Bayesiana; Cadeias de Markov de Monte Carlo (MCMC)
• Palavras-chave em Inglês: Weibull distribution; Odds ratio; Maximum likelihood estimator; Asymptotic theory; Bootstrap; Coverage probability; Bayesian inference; Monte Carlo Markov Chains (MCMC)
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/4558

The Weibull distribuition is a common initial choice for modeling data with monotone hazard rates. However, such distribution fails to provide a reasonable parametric _t when the hazard function is unimodal or bathtub-shaped. In this context, Cooray (2006) proposed a generalization of the Weibull family by considering the distributions of the odds of Weibull and inverse Weibull families, referred as the odd Weibull family which is not just useful for modeling unimodal and bathtub-shaped hazards, but it is also convenient for testing goodness-of-_t of Weibull and inverse Weibull as submodels. In this project we have systematically studied the odd Weibull family along with its properties, showing motivations for its utilization, inserting covariates in the model, pointing out some troubles associated with the maximum likelihood estimation and proposing interval estimation and hypothesis test construction methodologies for the model parameters. We have also compared resampling results with asymptotic ones. Coverage probability from proposed con_dence intervals and size and power of considered hypothesis tests were both analyzed as well via Monte Carlo simulation. Furthermore, we have proposed a Bayesian estimation methodology for the model parameters based in Monte Carlo Markov Chain (MCMC) simulation techniques.