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Math @ Duke
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Publications [#258068] of David B. Dunson
search arxiv.org.Papers Published
- Dunson, DB; Pillai, N; Park, JH, Bayesian density regression,
Journal of the Royal Statistical Society Series B Statistical Methodology, vol. 69 no. 2
(April, 2007),
pp. 163-183, WILEY, ISSN 1369-7412 [doi]
(last updated on 2026/01/15)
Abstract: 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.
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