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
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties