Markov Chain Monte Carlo Analysis of Correlated Count Data
AbstractThis 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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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 19 (2001)
Issue (Month): 4 (October)
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