On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation

• Autor(a) principal: Suzuki,A.K.
• Data de Publicação: 2013
• Outros Autores: Louzada,F., Cancho,V.G.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512013000300014
• topic: Bayesian approach; case deletion influence diagnostics; copula modeling; long-term survival

In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo 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 on artificial and real data.