The FGM bivariate lifetime copula model: a bayesian approach

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorSuzuki. A. K.
dc.creatorLouzada-Neto Neto, Franscisco
dc.creatorCancho, Vicente G.
dc.creatorBarriga, Gladys Dorotea Cacsire [UNESP]
dc.date2016-03-02T12:58:30Z
dc.date2016-03-02T12:58:30Z
dc.date2011
dc.date.accessioned2023-09-12T08:34:54Z
dc.date.available2023-09-12T08:34:54Z
dc.identifierhttp://www.pphmj.com/abstract/5794.htm
dc.identifierAdvances and Applications in Statistics, v. 21, p. 55-76, 2011.
dc.identifier0972-3617
dc.identifierhttp://hdl.handle.net/11449/134820
dc.identifier3503233632044163
dc.identifier5267593860042306
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8784059
dc.descriptionIn this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.
dc.descriptionUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Engenharia de Produção, Faculdade de Engenharia de Bauru, Bauru, Av. Eng. Luiz Edmundo C. Coube 14-01, CEP 17033-360, SP, Brasil
dc.descriptionUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Engenharia de Produção, Faculdade de Engenharia de Bauru, Bauru, Av. Eng. Luiz Edmundo C. Coube 14-01, CEP 17033-360, SP, Brasil
dc.format55-76
dc.languageeng
dc.relationAdvances and Applications in Statistics
dc.rightsAcesso restrito
dc.sourceCurrículo Lattes
dc.subjectCase deletion influence diagnostics
dc.subjectCopula modeling
dc.subjectSurvival data
dc.subjectBayesian approach
dc.titleThe FGM bivariate lifetime copula model: a bayesian approach
dc.typeArtigo


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