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| Author(s): |
Elizabeth Mie Hashimoto
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | Piracicaba. |
| Institution: | Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC) |
| Defense date: | 2013-03-15 |
| Examining board members: |
Edwin Moises Marcos Ortega;
Enrico Antônio Colosimo;
Gauss Moutinho Cordeiro;
Victor Hugo Lachos Davila;
Gilberto Alvarenga Paula
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| Advisor: | Edwin Moises Marcos Ortega |
| Abstract | |
Failure time data are characterized by the presence of censoring, which are observations that were not followed up until the occurrence of an event of interest. To study the behavior of the data of that nature, probability distributions are used. Furthermore, it is common to have one or more explanatory variables associated to failure times. Thus, the goal of this work is given to the generating of gamma distributions function in the context of regression models in survival analysis. This function has a shape parameter that allows create parametric families of distributions that are flexible to capture a wide variety of symmetrical and asymmetrical behaviors. Therefore, through the generating of gamma distributions function, the Weibull distribution and log-logistic distribution were modified to give two new probability distributions: gamma-Weibull and gammalog-logistic. Additionally, location-scale regression models, long-term models and models with random effects were also studied, considering the new distributions. For each of the proposed models, we used the maximum likelihood method to estimate the parameters and some diagnostic measures of global and local influence were calculated for possible influential points. However, residuals have been proposed for data with right censoring and interval-censored data and a simulation study to verify the empirical distribution of the residuals. Another issue explored is the introduction of models: gamma-Weibull inflated zeros and gamma-log-logistic inflated zeros, to analyze production data copaiba oil. Finally, different data set are used to illustrate the application of each of the models. (AU) | |
| FAPESP's process: | 10/04496-2 - Gamma-G regression model in survival analysis |
| Grantee: | Elizabeth Mie Hashimoto |
| Support Opportunities: | Scholarships in Brazil - Doctorate |