國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Bayesian semiparametric regression m...

Qin, Jun.

Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors.
作者:	Qin, Jun.
面頁冊數:	112 p.
附註:	Chair: Peter J. Lenk.
附註:	Source: Dissertation Abstracts International, Volume: 66-10, Section: A, page: 3721.
Contained By:	Dissertation Abstracts International66-10A.
標題:	Business Administration, Management.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3192758
ISBN:	9780542365737

Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors.
Qin, Jun.

Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors. - 112 p.

Chair: Peter J. Lenk.

Thesis (Ph.D.)--University of Michigan, 2005.

It has become more and more evident that under many circumstances assumptions used in parametric analysis about underlying populations from which the data are obtained are very restrictive. As more flexible methods than parametric approach, Bayesian nonparametric methods, such as Dirichlet Process and Polya Tree Priors, gained increasing popularity in applications in past few decades. Polya Trees have many good properties such as they are tractable and give probability one to absolutely continuous distributions. In applying Polya Tree priors for the error distribution in semiparametric regressions, moment constraints such as zero mean and (or) unit variance are required to identify the models. In my dissertation, I develop methodological and computational machinery of constrained Polya Tree priors. It is shown that constrained Polya Tree priors can put moment constraints on continuous and bound distributions of various shapes, such as distributions of multiple modes, skewness, etc. I intent to implement this method under various settings of statistical modelling, such as multivariate linear regression, survival models (or duration models), and discrete choice models. The simulation studies and applications on real data sets show that these semiparametric models lead to accurate parameter estimates and the estimated densities using constrained Polya Trees can incorporate data-driven features.

ISBN: 9780542365737Subjects--Topical Terms:

212493
Business Administration, Management.

Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors.
LDR:02379nmm _2200265 _450 001 170880
005 20061228142310.5
008 090528s2005 eng d
020 $a 9780542365737
035 $a 00242910
040 $a UnM $c UnM
100 0 $a Qin, Jun. $3 244911
245 1 0 $a Bayesian semiparametric regression models with mixtures of constrained Polya Tree priors.
300 $a 112 p.
500 $a Chair: Peter J. Lenk.
500 $a Source: Dissertation Abstracts International, Volume: 66-10, Section: A, page: 3721.
502 $a Thesis (Ph.D.)--University of Michigan, 2005.
520 # $a It has become more and more evident that under many circumstances assumptions used in parametric analysis about underlying populations from which the data are obtained are very restrictive. As more flexible methods than parametric approach, Bayesian nonparametric methods, such as Dirichlet Process and Polya Tree Priors, gained increasing popularity in applications in past few decades. Polya Trees have many good properties such as they are tractable and give probability one to absolutely continuous distributions. In applying Polya Tree priors for the error distribution in semiparametric regressions, moment constraints such as zero mean and (or) unit variance are required to identify the models. In my dissertation, I develop methodological and computational machinery of constrained Polya Tree priors. It is shown that constrained Polya Tree priors can put moment constraints on continuous and bound distributions of various shapes, such as distributions of multiple modes, skewness, etc. I intent to implement this method under various settings of statistical modelling, such as multivariate linear regression, survival models (or duration models), and discrete choice models. The simulation studies and applications on real data sets show that these semiparametric models lead to accurate parameter estimates and the estimated densities using constrained Polya Trees can incorporate data-driven features.
590 $a School code: 0127.
650 # 0 $a Business Administration, Management. $3 212493
650 # 0 $a Statistics. $3 182057
690 $a 0454
690 $a 0463
710 0 # $a University of Michigan. $3 212464
773 0 # $g 66-10A. $t Dissertation Abstracts International
790 $a 0127
790 1 0 $a Lenk, Peter J., $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3192758 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3192758