Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.

Journal Article (Journal Article)

This article considers a methodology for flexibly characterizing the relationship between a response and multiple predictors. Goals are (1) to estimate the conditional response distribution addressing the distributional changes across the predictor space, and (2) to identify important predictors for the response distribution change both within local regions and globally. We first introduce the probit stick-breaking process (PSBP) as a prior for an uncountable collection of predictor-dependent random distributions and propose a PSBP mixture (PSBPM) of normal regressions for modeling the conditional distributions. A global variable selection structure is incorporated to discard unimportant predictors, while allowing estimation of posterior inclusion probabilities. Local variable selection is conducted relying on the conditional distribution estimates at different predictor points. An efficient stochastic search sampling algorithm is proposed for posterior computation. The methods are illustrated through simulation and applied to an epidemiologic study.

Full Text

Duke Authors

Cited Authors

  • Chung, Y; Dunson, DB

Published Date

  • December 2009

Published In

Volume / Issue

  • 104 / 488

Start / End Page

  • 1646 - 1660

PubMed ID

  • 23580793

Pubmed Central ID

  • PMC3620660

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/jasa.2009.tm08302


  • eng