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Bayesian Model Averaging for Elastic...

Li, Nan.

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Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications./
Author:	Li, Nan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	110 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Contained By:	Dissertation Abstracts International79-05B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10685944
ISBN:	9780355534030

Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications.
Li, Nan.

Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 110 p.

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2017.

Researchers often build models in multiple regressions and then select the best one using certain variable selection methods. However, none of the existing approaches seem to deal with the uncertainty related to selected models and to balance overfitting and biased prediction in the sense of considering shrinkage and multicollinearity in variable selections as well as incorporating model uncertainty. In contrast, Bayesian model averaging (BMA) is proposed as a Bayesian approach to quantify uncertainty. BMA provides a way to tackle model uncertainty and becomes popular as a data analysis tool along with model selection process. In the meantime, regression regularization methods become more and more popular among statisticians for a more frequent appearance in high-dimensional problems. Regularized regression achieves simultaneous parameter estimation and variable selection by penalizing the model parameters and shrinking them towards zero.

ISBN: 9780355534030Subjects--Topical Terms:

517247
Statistics.

Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications.
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245 1 0 $a Bayesian Model Averaging for Elastic Net Using Regularization Path and its Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 110 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Advisers: Keying Ye; Da-Hsiang D. Lien.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2017.
520 $a Researchers often build models in multiple regressions and then select the best one using certain variable selection methods. However, none of the existing approaches seem to deal with the uncertainty related to selected models and to balance overfitting and biased prediction in the sense of considering shrinkage and multicollinearity in variable selections as well as incorporating model uncertainty. In contrast, Bayesian model averaging (BMA) is proposed as a Bayesian approach to quantify uncertainty. BMA provides a way to tackle model uncertainty and becomes popular as a data analysis tool along with model selection process. In the meantime, regression regularization methods become more and more popular among statisticians for a more frequent appearance in high-dimensional problems. Regularized regression achieves simultaneous parameter estimation and variable selection by penalizing the model parameters and shrinking them towards zero.
520 $a In this dissertation, we propose and investigate the elastic net shrinkage method under Bayesian model averaging for regression problems. This method combines the strength of the elastic net shrinkage and the Bayesian model averaging. It handles collinearity and model uncertainty simultaneously. We extend this method to variable selection by credible interval criteria. We develop the computational algorithm for the proposed method and compare its performance with lasso, elastic net, Bayesian lasso, Bayesian elastic net and Bayesian model averaging methods in simulation studies as well as in applications to real datasets. Results show that the proposed method works better than the existing ones in many situations. We also extend the proposed method to logistic regression, and the simulation studies show smaller classification error comparing to simple logistic regression. In addition, we incorporate proposed Bayesian elastic net averaging method with quantile regression in order to deal with skewed distribution.
590 $a School code: 1283.
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710 2 $a The University of Texas at San Antonio. $b Management Science & Statistics. $3 3170748
773 0 $t Dissertation Abstracts International $g 79-05B(E).
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10685944