Bayesian multivariate logistic regression.
Journal Article (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
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