國立虎尾科技大學 |

Variable selection through adaptive elastic net for proportional odds model.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Variable selection through adaptive elastic net for proportional odds model./
作者:	Wang, Chunxiang.
面頁冊數:	1 online resource (128 pages)
附註:	Source: Dissertations Abstracts International, Volume: 78-05, Section: B.
Contained By:	Dissertations Abstracts International78-05B.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369060942

Variable selection through adaptive elastic net for proportional odds model.
Wang, Chunxiang.

Variable selection through adaptive elastic net for proportional odds model. - 1 online resource (128 pages)

Source: Dissertations Abstracts International, Volume: 78-05, Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2016.

Includes bibliographical references

In building a proportional odds model, like other model building problems, the decision of which covariates to include in the final model has always been an important task for investigators. A successful variable selection can result in better risk assessment and model interpretation. For proportional odds model, variable selection is a more challenging task not only because of its nature of censored data, but also because of the unavailability of its partial likelihood. In this dissertation, we investigate the variable selection problem for proportional odds model. The proportional odds model fit by maximizing the marginal likelihood is proposed subject to the elastic net penalty. We also impose different weights on different coefficients so that important variables are most retained in the proposed model while the unimportant ones are most likely to be eliminated. This method combines the strength of the adaptively weighted lasso shrinkage and the quadratic regularization. It ensures the optimal large sample performance and handles collinearity simultaneously. We extend this method to ordinal regression with cumulative logit. We develop the computational algorithm for the proposed method and compare its performance with lasso, elastic net and adaptive lasso methods in simulation studies as well as in applications to real datasets. Results show that the proposed method works better than the existing ones.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9781369060942Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Adaptive elastic netIndex Terms--Genre/Form:

554714
Electronic books.

Variable selection through adaptive elastic net for proportional odds model.
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500 $a Source: Dissertations Abstracts International, Volume: 78-05, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Ko, Daijin.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2016.
504 $a Includes bibliographical references
520 $a In building a proportional odds model, like other model building problems, the decision of which covariates to include in the final model has always been an important task for investigators. A successful variable selection can result in better risk assessment and model interpretation. For proportional odds model, variable selection is a more challenging task not only because of its nature of censored data, but also because of the unavailability of its partial likelihood. In this dissertation, we investigate the variable selection problem for proportional odds model. The proportional odds model fit by maximizing the marginal likelihood is proposed subject to the elastic net penalty. We also impose different weights on different coefficients so that important variables are most retained in the proposed model while the unimportant ones are most likely to be eliminated. This method combines the strength of the adaptively weighted lasso shrinkage and the quadratic regularization. It ensures the optimal large sample performance and handles collinearity simultaneously. We extend this method to ordinal regression with cumulative logit. We develop the computational algorithm for the proposed method and compare its performance with lasso, elastic net and adaptive lasso methods in simulation studies as well as in applications to real datasets. Results show that the proposed method works better than the existing ones.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
653 $a Adaptive elastic net
653 $a Adaptive lasso
653 $a Lasso
653 $a Proportional odds model
653 $a Variable selection
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