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

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dc.contributor.advisorYe, Keying
dc.contributor.advisorLien, Da-Hsiang D.
dc.contributor.authorLi, Nan
dc.contributor.committeeMemberYe, Keying
dc.contributor.committeeMemberLien, Da-Hsiang D.
dc.contributor.committeeMemberTripathi, Ram
dc.contributor.committeeMemberHan, David
dc.date.accessioned2024-02-12T14:54:05Z
dc.date.available2024-02-12T14:54:05Z
dc.date.issued2017
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.abstractResearchers 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. 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.
dc.description.departmentManagement Science and Statistics
dc.format.extent110 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9780355534030
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4403
dc.languageen
dc.subjectBayesian Elastic Net Averaging
dc.subjectBayesian Model Averaging
dc.subjectElastic Net
dc.subjectLogistic regression
dc.subjectQuantile regression
dc.subjectRegularization
dc.subject.classificationStatistics
dc.titleBayesian Model Averaging for Elastic Net Using Regularization Path and its Applications
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentManagement Science and Statistics
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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