1. Universidade Federal do Rio Grande do Norte
2. Centro de Ciências Exatas e da Terra
3. DEST - Departamento de Estatística
4. CCET - DEST - Artigos publicados em periódicos

Título:	Gradient and likelihood ratio tests in cure rate models
Autor(es):	Carneiro, Hérica P. A Valença, Dione M.
Palavras-chave:	Survival analysis;Unified model;Promotion time model;Gradient statistic
Data do documento:	11-Jun-2016
Editor:	Canadian Center of Science and Education
Referência:	CARNEIRO, Hérica P. A. ; VALENÇA, Dione M. Gradient and Likelihood Ratio Tests in Cure Rate Models. International Journal of Statistics and Probability, v. 5, n.4, p. 9, 2016. Disponível em: <http://www.ccsenet.org/journal/index.php/ijsp/article/download/58419/33303>. Acesso em: 06 dez. 2017.
Resumo:	In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate models take this fact into account. They have been extensively studied for several authors who have proposed extensions and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially the likelihood ratio. Recently a new test called gradient test has been proposed. The gradient statistic shares the same asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the gradient test in finite samples and compare it with the classic tests in different models. However little is known about the properties of these large sample tests in finite sample for cure rate models. In this work we performed a simulation study based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient tests in finite samples. An application is presented to illustrate the results.
URI:	https://repositorio.ufrn.br/jspui/handle/123456789/27095
ISSN:	1927-7040
Aparece nas coleções:	CCET - DEST - Artigos publicados em periódicos

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