Markov chain Monte Carlo analysis of correlated count data

被引：162
|
作者：
Chib, S [1 ]
Winkelmann, R
机构：
[1] Washington Univ, John M Olin Sch Business, St Louis, MO 63130 USA
[2] IZA Bonn, D-53072 Bonn, Germany
来源：
JOURNAL OF BUSINESS & ECONOMIC STATISTICS | 2001年 / 19卷 / 04期
关键词：
latent effects; Metropolis-Hastings algorithm; multivariate count data; Poisson-lognormal distribution;
D O I：
10.1198/07350010152596673
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：428 / 435
页数：8
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