Bayesian Inference of Hierarchical Regression Model for zero-Inflated Clustered Count Data

Zero-inflated Poisson (ZIP) regression model is a popular tool for analyzing count data with excess zeros. In this paper, a flexible hierarchical ZIP regression model is proposed to handle with such data with cluster and Bayesian approach is develop. A Gibbs sampler is employed to produce the Bayesian estimate, a goodness-of-fit and a Bayesian information criterion (BIC) are used for model comparison and selection. Finally, an application of data from a ship damage incident study illustrates the proposed method.