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Bayesian density regression and pred...

Park, Ju-Hyun.

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Bayesian density regression and predictor-dependent clustering.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian density regression and predictor-dependent clustering./
Author:	Park, Ju-Hyun.
Description:	99 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3930.
Contained By:	Dissertation Abstracts International69-07B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315717
ISBN:	9780549671190

Bayesian density regression and predictor-dependent clustering.
Park, Ju-Hyun.

Bayesian density regression and predictor-dependent clustering. - 99 p.

Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3930.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2008.

Mixture models are widely used in many application areas, with finite mixtures of Gaussian distributions applied routinely in clustering and density estimation. With the increasing need for a flexible model for predictor-dependent clustering and conditional density estimation, mixture models are generalized to incorporate predictors with infinitely many components in the semiparametric Bayesian perspective. Much of the recent work in the nonparametric Bayes literature focuses on introducing predictor-dependence into the probability weights.

ISBN: 9780549671190Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian density regression and predictor-dependent clustering.
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005 20110110132553.5
008 130515s2008 ||||||||||||||||| ||eng d
020 $a 9780549671190
035 $a (UMI)AAI3315717
035 $a AAI3315717
040 $a UMI $c UMI
100 1 $a Park, Ju-Hyun. $3 1669601
245 1 0 $a Bayesian density regression and predictor-dependent clustering.
300 $a 99 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3930.
500 $a Adviser: David Dunson.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2008.
520 $a Mixture models are widely used in many application areas, with finite mixtures of Gaussian distributions applied routinely in clustering and density estimation. With the increasing need for a flexible model for predictor-dependent clustering and conditional density estimation, mixture models are generalized to incorporate predictors with infinitely many components in the semiparametric Bayesian perspective. Much of the recent work in the nonparametric Bayes literature focuses on introducing predictor-dependence into the probability weights.
520 $a In this dissertation we propose three semiparametric Bayesian methods, with a focus on the applications of predictor-dependent clustering and condition density estimation. We first derive a generalized product partition model (GPPM), starting with a Dirichlet process (DP) mixture model. The GPPM results in a generalized Polya urn scheme. Next, we consider the problem of density estimation in cases where predictors are not directly measured. We propose a model that relies on Bayesian approaches to modeling of the unknown distribution of latent predictors and of the conditional distribution of responses given latent predictors. Finally, we develop a semiparametric Bayesian model for density regression in cases with many predictors. To reduce dimensionality of data, our model is based on factor analysis models with the number of latent variables unknown. A nonparametric prior for infinite factors is defined.
590 $a School code: 0153.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 $a The University of North Carolina at Chapel Hill. $b Biostatistics. $3 1023527
773 0 $t Dissertation Abstracts International $g 69-07B.
790 1 0 $a Dunson, David, $e advisor
790 1 0 $a Herring, Amy $e committee member
790 1 0 $a Kosorok, Michael $e committee member
790 1 0 $a Zhu, Hongtu $e committee member
790 1 0 $a Bollen, Kenneth $e committee member
790 $a 0153
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315717