CONVERGENCE DIAGNOSTICS IN MCMC FOR ZERO-INFLATED POISSON MODELS FOR AIR POLLUTION DATA

Abstract

Convergence diagnostics help to decide whether the Markov chain has reached its stationary and to determine the number of iterations to keep after the Markov chain has reached stationary. There are no conclusive tests that can tell you when the Markov chain has converged to its stationary distribution. In this study, we examine some convergence diagnostics for zero inflated Poisson models for air pollution data.