Markov chain Monte Carlo analysis of correlated count data

Siddhartha Chib; Rainer Winkelmann

doi:10.1198/07350010152596673

Markov chain Monte Carlo analysis of correlated count data

Siddhartha Chib
, Rainer Winkelmann

Research output: Contribution to journal › Article › peer-review

184 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	428-435
Number of pages	8
Journal	Journal of Business and Economic Statistics
Volume	19
Issue number	4
DOIs	https://doi.org/10.1198/07350010152596673
State	Published - Oct 2001

Keywords

Latent effects
Metropolis-hastings algorithm
Multivariate count data
Poisson-lognormal distribution

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10.1198/07350010152596673

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abstract = "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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AB - 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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KW - Metropolis-hastings algorithm

KW - Multivariate count data

KW - Poisson-lognormal distribution

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Markov chain Monte Carlo analysis of correlated count data

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Keywords

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