期刊
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
卷 19, 期 4, 页码 428-435出版社
TAYLOR & FRANCIS INC
DOI: 10.1198/07350010152596673
关键词
latent effects; Metropolis-Hastings algorithm; multivariate count data; Poisson-lognormal distribution
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.
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