Bayesian density regression

Published

Journal Article

The paper considers Bayesian methods for density regression, allowing a random probability distribution to change flexibly with multiple predictors. The conditional response distribution is expressed as a non-parametric mixture of regression models, with the mixture distribution changing with predictors. A class of weighted mixture of Dirichlet process priors is proposed for the uncountable collection of mixture distributions. It is shown that this specification results in a generalized Pólya urn scheme, which incorporates weights that are dependent on the distance between subjects' predictor values. To allow local dependence in the mixture distributions, we propose a kernel-based weighting scheme. A Gibbs sampling algorithm is developed for posterior computation. The methods are illustrated by using simulated data examples and an epidemiologic application. © Royal Statistical Society.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Pillai, N; Park, JH

Published Date

  • April 1, 2007

Published In

Volume / Issue

  • 69 / 2

Start / End Page

  • 163 - 183

Electronic International Standard Serial Number (EISSN)

  • 1467-9868

International Standard Serial Number (ISSN)

  • 1369-7412

Digital Object Identifier (DOI)

  • 10.1111/j.1467-9868.2007.00582.x

Citation Source

  • Scopus