Variable selection through adaptive elastic net for proportional odds model

dc.contributor.advisorKo, Daijin
dc.contributor.authorWang, Chunxiang
dc.contributor.committeeMemberYe, Keying
dc.contributor.committeeMemberTripathi, Ram
dc.contributor.committeeMemberGaufo, Gary
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractIn 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.
dc.description.departmentManagement Science and Statistics
dc.format.extent128 pages
dc.subjectAdaptive Elastic Net
dc.subjectAdaptive Lasso
dc.subjectProportional Odds Model
dc.subjectVariable Selection
dc.subject.lcshRegression analysis -- Mathematical models
dc.subject.lcshMathematical statistics
dc.titleVariable selection through adaptive elastic net for proportional odds model
dcterms.accessRightspq_closed Science and Statistics of Texas at San Antonio of Philosophy


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