22/03/2019 - A flexible cure rate model based on the polylogarithm distribution - Palestrante: Diego I. Gallardo Mateluna (Universidad de Atacama - Chile)
| Quando |
22/03/2019 das 14h00 até 18h00 |
|---|---|
| Onde | Auditorio Luiz Antônio Fávaro (sala 4-111) |
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Seminário Conjunto UFSCar/ICMC
Data e horário: 22/03/2019 às 14 horas
Local: Auditorio Luiz Antônio Fávaro (sala 4-111)
Título: A flexible cure rate model based on the polylogarithm distribution
Author: Diego I. Gallardo Mateluna*.
Departamento de Matemáticas, Universidad de Atacama, Chile.
Models for dealing with survival data in the presence of a cured fraction of individuals have attracted the attention of many researchers and practitioners in recent years. In this paper, we propose a cure rate model under the competing risks scenario. For the number of causes that can lead to the event of interest, we assume the polylogarithm distribution. The model is flexible in the sense it encompasses some well known models, which can be tested using large sample test statistics applied to nested models. Maximum likelihood estimation based on the EM algorithm and hypotheses testing are investigated. Results of simulation studies designed to gauge the performance of the estimation method and of two test statistics are reported.
Data e horário: 22/03/2019 às 14 horas
Local: Auditorio Luiz Antônio Fávaro (sala 4-111)
Título: A flexible cure rate model based on the polylogarithm distribution
Author: Diego I. Gallardo Mateluna*.
Departamento de Matemáticas, Universidad de Atacama, Chile.
Models for dealing with survival data in the presence of a cured fraction of individuals have attracted the attention of many researchers and practitioners in recent years. In this paper, we propose a cure rate model under the competing risks scenario. For the number of causes that can lead to the event of interest, we assume the polylogarithm distribution. The model is flexible in the sense it encompasses some well known models, which can be tested using large sample test statistics applied to nested models. Maximum likelihood estimation based on the EM algorithm and hypotheses testing are investigated. Results of simulation studies designed to gauge the performance of the estimation method and of two test statistics are reported.
The methodology is applied in the analysis of a data set.
* Joint work with
Yolanda M. Gómez. Departamento de matemáticas, Universidad de Atacama, Chile.
Mário de Castro. Instituto de Ciências matemáticas e de Computação, USP-Campus São Carlos, Brazil.