The Long-Term Bivariate Survival FGM Copula Model: An Application to a Brazilian HIV Data
Francisco Louzada, Adriano Kamimura Suzuki, Vicente G. Cancho, F ́atima L. Prince, Gilberto A. Pereira · Journal of Data Science · 2021
In this paper we propose a new bivariate long-term distribution based on the Farlie-Gumbel-Morgenstern copula model. The proposed model allows for the presence of censored data and covariates in the cure parameter. 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 develop a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real HIV data.