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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Markov chain Monte Carlo analysis of correlated count data |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Journal of Business & Economic Statistics |
| Publisher | American Statistical Association |
| Geographical Reach | international |
| ISSN | 0735-0015 |
| Volume | 19 |
| Number | 4 |
| Page Range | 428 - 435 |
| Date | 2001 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1198/07350010152596673 |
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