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Markov chain Monte Carlo analysis of correlated count data


Chib, Siddhartha; Winkelmann, Rainer (2001). Markov chain Monte Carlo analysis of correlated count data. Journal of Business & Economic Statistics, 19(4):428-435.

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.

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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90 citations in Web of Science®
98 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:2001
Deposited On:11 Feb 2008 12:21
Last Modified:05 Apr 2016 12:17
Publisher:American Statistical Association
ISSN:0735-0015
Publisher DOI:10.1198/07350010152596673
Permanent URL: http://doi.org/10.5167/uzh-1146

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