Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.
Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.
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DOI:
10.1198/jasa.2009.tm08302
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发表时间:
2009-12-01
影响因子:
3.7
通讯作者:
Dunson DB
中科院分区:
文献类型:
--
作者:
Chung Y;Dunson DB
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.
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DOI:
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发表时间:
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