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dc.contributor.author Park, JH
dc.contributor.author Dunson, DB
dc.date.accessioned 2011-06-21T17:32:25Z
dc.date.issued 2010-07-01
dc.identifier.citation Statistica Sinica, 2010, 20 (3), pp. 1203 - 1226
dc.identifier.issn 1017-0405
dc.identifier.uri http://hdl.handle.net/10161/4623
dc.description.abstract Starting with a carefully formulated Dirichlet process (DP) mixture model, we derive a generalized product partition model (GPPM) in which the partition process is predictor-dependent. The GPPM generalizes DP clustering to relax the exchangeability assumption through the incorporation of predictors, resulting in a generalized Pólya urn scheme. In addition, the GPPM can be used for formulating flexible semiparametric Bayes models for conditional distribution estimation, bypassing the need for expensive computation of large numbers of unknowns characterizing priors for dependent collections of random probability measures. A variety of special cases are considered, and an efficient Gibbs sampling algorithm is developed for posterior computation. The methods are illustrated using simulation examples and an epidemiologic application.
dc.format.extent 1203 - 1226
dc.language.iso en_US en_US
dc.relation.ispartof Statistica Sinica
dc.title Bayesian generalized product partition model
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-7-0 en_US
duke.description.endpage 1226 en_US
duke.description.issue 3 en_US
duke.description.startpage 1203 en_US
duke.description.volume 20 en_US
dc.relation.journal Statistica Sinica en_US
pubs.issue 3
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers/Duke Institute for Brain Sciences
pubs.organisational-group /Duke/Pratt School of Engineering
pubs.organisational-group /Duke/Pratt School of Engineering/Electrical and Computer Engineering
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Mathematics
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 20

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