Bayesian Variable Selection for Zero-inflated Longitudinal Count Data

In this paper, we present a Bayesian variable selection method for zero-inflated longitudinal data. For this purpose, we consider a zero-inflated power series random effects model that includes the zero-inflated Poisson and negative binomial random effects models. We propose using continuous spike and Dirac spike priors to simultaneously estimate the regression coefficients and select the important covariate variables. We apply the MCMC method using Gibbs sampling for posterior inference. Some simulation studies are performed to investigate the performance of the proposed approach, and it is also applied to analyze a real dataset from the RAND Health Insurance Experiment.