On estimation and influence diagnostics for a Bivariate Promotion Lifetime Model Based on the FGM Copula: A Fully Bayesian Computation

Abstract

ABSTRACT. 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.

Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data

“…This is explored by many authors in survival analysis. Among them are: Romeo et al [ 7 ] and da Cruz et al [ 8 ], who considered the Archimedean copula family; Louzada et al [ 9 ] and Suzuki et al [ 10 ], who considered the Farlie–Gumbel–Morgenstern (FGM) copula; and Romeo et al [ 11 ], who considered the two-parameter Archimedean family of power variance function (PVF) copulas.…”