Modelos semiparamétricos para análise de eventos recorrentes

• Ano de defesa: 2019
• Autor(a) principal: Rumenick Pereira da Silva
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICEX - INSTITUTO DE CIÊNCIAS EXATAS; Programa de Pós-Graduação em Estatística; UFMG
• Palavras-chave em Português: Processo de Poisson; Processo de renovação; Classe geral; Polinômios de Bernstein; Exponencial por partes; Inferência frequentista; Inferência Bayesiana; Amostrador de Gibbs; JAGS; Estatística - Teses; Poisson, Processos de - Teses; Teoria bayesiana de decisão estatística - Teses; Polinômios de Bernstein - Teses; Amostrador de Gibbs - Teses
• Link de acesso: http://hdl.handle.net/1843/48520

In areas such as medicine, public health, business, industry, reliability, social sciences and insurance, many situations arise in which the interest is to study processes that generate events repeatedly over time. These types of situations are called recurrent event processes, and the data they provide is called recurrent event data. In this context, the models proposed in the present work are, fundamentally, survival models based on the Poisson process and the renewal process, with the hazard function (or intensity) being constructed from a semiparametric perspective via Bernstein polynomials. In addition, two general classes of semiparametric models are proposed that have the above processes as particular cases, the hazards functions (or intensities) of these classes being obtained through the Bernstein polynomials and the piecewise exponential. The proposed models are flexible in the sense that they do not impose a specific form for the hazard function (or intensity), have qualities similar to those of the parametric models with regard to the estimation of the survivor, hazard (or intensity) and cumulative hazard (or intensity) functions. Some of these models do not assume proportional hazards (or intensities) and have computational characteristics that are attractive from the point of view of classical and Bayesian inference, which motivated to make inference for the models proposed under both paradigms. The analysis developed here presents the results of a simulation study aimed at investigating the behavior of the proposed models in different scenarios and also explores real data from classic studies in the literature for the analysis of recurring events.