Bayesian inference and diagnostics in zero-inflated generalized power series regression model

Abstract

The paper provides a Bayesian analysis for the zero-inflated regression models based on the generalized power series distribution. The approach is based on Markov chain Monte Carlo methods. The residual analysis is discussed and case-deletion influence diagnostics are developed for the joint posterior distribution, based on the -divergence, which includes several divergence measures such as the Kullback-Leibler, J-distance, L-1 norm, and (2)-square in zero-inflated general power series models. The methodology is reflected in a data set collected by wildlife biologists in a state park in California.