On estimation and influence diagnostics for a Bivariate Promotion Lifetime Model Based on the FGM Copula: A Fully Bayesian Computation - Suzuki, Adriano Kamimura; Louzada, Francisco; Cancho, Vicente Garibay (Trends in Computational and Applied Mathematics, 2013)
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.