Bayesian multivariate logistic regression.

Sean M O'Brien; David B Dunson

doi:10.1111/j.0006-341x.2004.00224.x

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Bayesian multivariate logistic regression.

Sean M O'Brien·David B Dunson

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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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Sean M O'Brien, David B Dunson. 2004. Bayesian multivariate logistic regression.. https://doi.org/10.1111/j.0006-341x.2004.00224.x

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