Markov chain Monte Carlo analysis of correlated count data

This article is concerned with the analysis of correlated count data. A cla ss of models is proposed in which the correlation among the counts is repre sented by correlated latent effects. Special cases of the model are discuss ed and a tuned and efficient Markov chain Monte Carlo algorithm is develope d to estimate the model under both multivariate normal and multivariate-t a ssumptions on the latent effects. The methods are illustrated with two real data examples of six and sixteen variate correlated counts.