Two essays on Birnbaum-Saunders regression models for censored data

• Autor(a) principal: Sousa, Mário Fernando de
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/001300000drfm
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/7235
• topic: Censored data; Birnbaum-Saunders distribution; Tobit models; Maximum likelihood estimation; Diagnostics; Mixture models; Dados censurados; Distribuição Birnbaum-Saunders; Modelos tobit; Modelos de mistura; Máxima verossimilhança; Diagnósticos; CIENCIAS SOCIAIS APLICADAS::ECONOMIA

This work aims to fill a gap in the literature on modeling asymmetric and censored data. The main objective is to provide a contribution by developing two models, which will be presented in two papers, respectively. In the first paper, we develop the tobit-Birnbaum-Saunders model, a variation of the standard tobit model. We discuss estimation based on the maximum likelihood method, residuals, diagnostic techniques and an empirical application. In the second paper, we propose the use of a mixture between the Birnbaum-Saunders and Bernoulli distributions. The objective is to generalize the tobit-Birnbaum-Saunders model in order to consider the possibility of partial observations below a cutoff point. For the mixture model, we carry out a Monte Carlo simulation study and an empirical application. The results show that, in both cases, the Birnbaum-Saunders distribution provides the best results.