Markov Chain Monte Carlo Analysis of Correlated Count Data
提出一类模型，用相关潜在效应表示计数数据间的相关性，并开发了高效的马尔可夫链蒙特卡洛算法进行估计，适用于多元正态和多元t分布假设，通过两个实际数据示例展示方法。
This article is concerned with the analysis of correlated count data. A class of models is proposed in which the correlation among the counts is represented by correlated latent effects. Special cases of the model are discussed and a tuned and efficient Markov chain Monte Carlo algorithm is developed to estimate the model under both multivariate normal and multivariate-t assumptions on the latent effects. The methods are illustrated with two real data examples of six and sixteen variate correlated counts.