Nonparametric Bayes Conditional Distribution Modeling With Variable Selection.
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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.
SubjectConditional distribution estimation
Kernel stick-breaking process
Mixture of experts
Stochastic search variable selection
Published Version (Please cite this version)10.1198/jasa.2009.tm08302
Publication InfoChung, Yeonseung; & Dunson, David B (2009). Nonparametric Bayes Conditional Distribution Modeling With Variable Selection. J Am Stat Assoc, 104(488). pp. 1646-1660. 10.1198/jasa.2009.tm08302. Retrieved from https://hdl.handle.net/10161/4398.
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Arts and Sciences Distinguished Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecol