Família Kumaraswamy-G para analisar dados de sobrevivência de longa duração

• Ano de defesa: 2015
• Autor(a) principal: Eudes, Amanda Morales
• Orientador(a): Tomazella, Vera Lucia Damasceno
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
• País: BR
• Palavras-chave em Português: Análise de sobrevivência; Kumaraswamy generalizada; Abordagem bayesiana; Fração de cura
• Palavras-chave em Inglês: Survival analysis; Kumaraswamy generalized; Bayesian approach; Long term model; Covariates
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/4602

In survival analysis is studied the time until the occurrence of a particular event of interest and in the literature, the most common approach is parametric, where the data follow a specific probability distribution. Various known distributions maybe used to accommodate failure time data, however, most of these distributions are not able to accommodate non-monotonous hazard functions. Kumaraswamy (1980) proposed a new probability distribution and, based on that, recently Cordeiro and de Castro (2011) proposed a new family of generalized distributions, the so-called Kumaraswamy generalized (Kum-G). In addition to its flexibility, this distribution may also be considered for unimodal and tub shaped hazard functions. The objective of this dissertation is to present the family of Kum-G distributions and their particular cases to analyze lifetime data of individuals at risk, considering that part of the population will never present the event of interest, and considering that covariates may influence the survival function and the cured proportion of the population. Some properties of these models will be discussed as well as appropriate estimation methods, in the classical and Bayesian approaches. Finally, applications of such models are presented to literature data sets.