Suzuki. A. K.Louzada-Neto Neto, FransciscoCancho, Vicente G.Barriga, Gladys Dorotea Cacsire [UNESP]2016-03-022016-03-022011Advances and Applications in Statistics, v. 21, p. 55-76, 2011.0972-3617http://hdl.handle.net/11449/134820In 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.55-76engCase deletion influence diagnosticsCopula modelingSurvival dataBayesian approachArtigoAcesso restrito35032336320441635267593860042306