Bayesian analysis of correlated proportions
| dc.contributor.author | Kateri, M. | en |
| dc.contributor.author | Papaioannou, T. | en |
| dc.contributor.author | Dellaportas, P. | en |
| dc.date.accessioned | 2015-11-24T17:22:13Z | |
| dc.date.available | 2015-11-24T17:22:13Z | |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/12579 | |
| dc.rights | Default Licence | - |
| dc.subject | Bayes factor, empirical bayes, Gibbs variable selection, hierarchical | en |
| dc.subject | logistic regression, highest posterior density region, matched pairs, Markov chain Monte | en |
| dc.subject | Carlo. | en |
| dc.title | Bayesian analysis of correlated proportions | en |
| heal.abstract | In this paper we present a Bayesian analysis of 2£2 contingency tables, corresponding to matched pairs designs. We provide Bayes and empirical Bayes estimates for the cell probabilities of these tables as well as the Bayes factor for testing the equality of correlated proportions. The approximate highest posterior density (HPD) region for the difference of the correlated proportions is also obtained. Finally, a Bayesian variable selection approach is applied to a hierarchical logistic regression model and posterior model probabilities for the equality of the correlated proportions are estimated. This latter approach has the feature that the posterior model probabilities depend on the maindiagonal cells. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.journalName | The Indian Journal of Statistics | en |
| heal.journalType | peer reviewed | - |
| heal.publicationDate | 2001 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μαθηματικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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