Bayesian multivariate logistic regression.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • O'Brien, SM; Dunson, DB

Published Date

  • September 2004

Published In

Volume / Issue

  • 60 / 3

Start / End Page

  • 739 - 746

PubMed ID

  • 15339297

Pubmed Central ID

  • 15339297

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.0006-341X.2004.00224.x

Language

  • eng

Conference Location

  • United States