東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Nonparametric Bayesian Quantile Regr...

Chang, Chao.

Linked to FindBook

Google Book

Amazon

博客來

Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models./
Author:	Chang, Chao.
Description:	149 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
Contained By:	Dissertation Abstracts International76-09B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3700031
ISBN:	9781321698824

Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.
Chang, Chao.

Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. - 149 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.

Thesis (Ph.D.)--Washington University in St. Louis, 2015.

This item is not available from ProQuest Dissertations & Theses.

We propose new nonparametric Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models. All the existing quantile regression methods based on DPMs require the kernel density to satisfy the quantile constraint, hence the kernel densities are themselves usually in the form of mixtures. One innovation of our approaches is that we impose no constraint on the kernel, thus a wide range of densities can be chosen as the kernels of the DPM model. The quantile constraint is satisfied by a post-processing of the DPM by a suitable location shift. As a result, our proposed models use simpler kernels and yet possess great flexibility by mixing over both the location parameter and the scale parameter. The posterior consistency of our proposed model is studied carefully. And Markov chain Monte Carlo algorithms are provided for posterior inference. The performance of our approaches is evaluated using simulated data and real data. Moreover, we are able to incorporate random effects into our models such that our approaches can be extended to handle longitudinal data.

ISBN: 9781321698824Subjects--Topical Terms:

517247
Statistics.

Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.
LDR:02068nmm a2200289 4500 001 2073315
005 20160915132425.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321698824
035 $a (MiAaPQ)AAI3700031
035 $a AAI3700031
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chang, Chao. $3 3188552
245 1 0 $a Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.
300 $a 149 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: B.
500 $a Adviser: Nan Lin.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a We propose new nonparametric Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models. All the existing quantile regression methods based on DPMs require the kernel density to satisfy the quantile constraint, hence the kernel densities are themselves usually in the form of mixtures. One innovation of our approaches is that we impose no constraint on the kernel, thus a wide range of densities can be chosen as the kernels of the DPM model. The quantile constraint is satisfied by a post-processing of the DPM by a suitable location shift. As a result, our proposed models use simpler kernels and yet possess great flexibility by mixing over both the location parameter and the scale parameter. The posterior consistency of our proposed model is studied carefully. And Markov chain Monte Carlo algorithms are provided for posterior inference. The performance of our approaches is evaluated using simulated data and real data. Moreover, we are able to incorporate random effects into our models such that our approaches can be extended to handle longitudinal data.
590 $a School code: 0252.
650 4 $a Statistics. $3 517247
650 4 $a Mathematics. $3 515831
690 $a 0463
690 $a 0405
710 2 $a Washington University in St. Louis. $b Mathematics. $3 1681717
773 0 $t Dissertation Abstracts International $g 76-09B(E).
790 $a 0252
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3700031