Math @ Duke

Publications [#257931] of David B. Dunson
search arxiv.org.Papers Published
 O'Brien, SM; Dunson, DB, Bayesian multivariate logistic regression.,
Biometrics, vol. 60 no. 3
(September, 2004),
pp. 739746, ISSN 0006341X [15339297], [doi]
(last updated on 2019/05/19)
Abstract: Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic distribution that can be used to construct a likelihood for multivariate logistic regression analysis of binary and categorical data. The model for individual outcomes has a marginal logistic structure, simplifying interpretation. We follow a Bayesian approach to estimation and inference, developing an efficient data augmentation algorithm for posterior computation. The method is illustrated with application to a neurotoxicology study.


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