Semi-parametric generalized log-gamma regression models

• Ano de defesa: 2017
• Autor(a) principal: Delgado, Carlos Alberto Cardozo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Distribuição log-gamma generalizada; Distribuições assimétricas; Estimação por máxima verossimilhança penalizada; Modelos semiparamétricos; Observações censuradas; P-splines; Spline cúbico natural
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-15032018-185352/

The central objective of this work is to develop statistical tools for semi-parametric regression models with generalized log-gamma errors under the presence of censored and uncensored observations. The estimates of the parameters are obtained through the multivariate version of Newton-Raphson algorithm and an adequate combination of Fisher Scoring and Backffitting algorithms. Through analytical tools and using simulations the properties of the penalized maximum likelihood estimators are studied. Some diagnostic techniques such as quantile and deviance-type residuals as well as local influence measures are derived. The methodologies are implemented in the statistical computational environment R. The package sglg is developed. Finally, we give some applications of the models to real data.