{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109509"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109509","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian variable selection in high dimensional censored regression models","abstract":"The development in technologies drives research in variable selection in various fields, especially in bio-medical areas where high-dimensional gene expression data are present. Various approaches have been developed for associating patients' data with patients' survival times, however, not many can deal with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative algorithm for accelerated failure models (AFT models) with censored survival data under no distributional assumption. Unlike existing methods, the proposed method is able to handle high-dimensional variable selection with less stringent assumption which adopts the scalable feature from Bayesian framework with a continuous spike-and-slab prior specification. We show that the method can be further extended to bivariate survival data by assuming independence between the two events such that the connections between events are carried in the join prior distribution of the unknown coefficient matrix. Lastly, we work with a relatively new regression model, named Restricted Mean Survival Times (RMST) regression models, targeting at variable selection problem when the proportional hazards assumption is invalid and prediction problems for RMST. With a spike-and-slab Lasso prior, we transform the Bayesian variable selection framework of the RMST regression model to a penalized logistic regression problem with a proper choice of the link function. The proposed work can be treated as a more generalized variable selection method comparing to either Cox model or AFT model when the true model is misspecified.","abstract_html":"The development in technologies drives research in variable selection in various fields, especially in bio-medical areas where high-dimensional gene expression data are present. Various approaches have been developed for associating patients' data with patients' survival times, however, not many can deal with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative algorithm for accelerated failure models (AFT models) with censored survival data under no distributional assumption. Unlike existing methods, the proposed method is able to handle high-dimensional variable selection with less stringent assumption which adopts the scalable feature from Bayesian framework with a continuous spike-and-slab prior specification. We show that the method can be further extended to bivariate survival data by assuming independence between the two events such that the connections between events are carried in the join prior distribution of the unknown coefficient matrix. Lastly, we work with a relatively new regression model, named Restricted Mean Survival Times (RMST) regression models, targeting at variable selection problem when the proportional hazards assumption is invalid and prediction problems for RMST. With a spike-and-slab Lasso prior, we transform the Bayesian variable selection framework of the RMST regression model to a penalized logistic regression problem with a proper choice of the link function. The proposed work can be treated as a more generalized variable selection method comparing to either Cox model or AFT model when the true model is misspecified.","abstract_has_math":false,"creators":["Yin, Wenjing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Liang, Feng","Narisetty, Naveen Naidu","Zhao, Sihai Dave","Douglas, Jeffrey A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:42:41Z","date_published":"2021-03-05T21:42:41Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Bayesian Variable Selection","Survival Analysis","Censored Regression Models","Spike-and-slab Priors"],"languages":["en"],"rights":["Copyright 2020 Wenjing Yin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109509","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Feng","Narisetty, Naveen Naidu","Zhao, Sihai Dave","Douglas, Jeffrey A"]},{"key":"dc:creator","label":"Author","values":["Yin, Wenjing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:42:41Z","2023-03-05T21:43:00Z","2020-11-30","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bayesian Variable Selection","Survival Analysis","Censored Regression Models","Spike-and-slab Priors"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Wenjing Yin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109509"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The development in technologies drives research in variable selection in various fields, especially in bio-medical areas where high-dimensional gene expression data are present. Various approaches have been developed for associating patients' data with patients' survival times, however, not many can deal with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative algorithm for accelerated failure models (AFT models) with censored survival data under no distributional assumption. Unlike existing methods, the proposed method is able to handle high-dimensional variable selection with less stringent assumption which adopts the scalable feature from Bayesian framework with a continuous spike-and-slab prior specification. We show that the method can be further extended to bivariate survival data by assuming independence between the two events such that the connections between events are carried in the join prior distribution of the unknown coefficient matrix. Lastly, we work with a relatively new regression model, named Restricted Mean Survival Times (RMST) regression models, targeting at variable selection problem when the proportional hazards assumption is invalid and prediction problems for RMST. With a spike-and-slab Lasso prior, we transform the Bayesian variable selection framework of the RMST regression model to a penalized logistic regression problem with a proper choice of the link function. The proposed work can be treated as a more generalized variable selection method comparing to either Cox model or AFT model when the true model is misspecified.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-12-01","The student, Wenjing Yin, accepted the attached license on 2020-11-24 at 13:32.","The student, Wenjing Yin, submitted this Dissertation for approval on 2020-11-24 at 13:52.","This Dissertation was approved for publication on 2020-11-30 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15954 on 2021-03-04 at 16:19:52","Made available in DSpace on 2021-03-05T21:42:41Z (GMT). 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Various approaches have been developed for associating patients' data with patients' survival times, however, not many can deal with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative algorithm for accelerated failure models (AFT models) with censored survival data under no distributional assumption. Unlike existing methods, the proposed method is able to handle high-dimensional variable selection with less stringent assumption which adopts the scalable feature from Bayesian framework with a continuous spike-and-slab prior specification. We show that the method can be further extended to bivariate survival data by assuming independence between the two events such that the connections between events are carried in the join prior distribution of the unknown coefficient matrix. Lastly, we work with a relatively new regression model, named Restricted Mean Survival Times (RMST) regression models, targeting at variable selection problem when the proportional hazards assumption is invalid and prediction problems for RMST. With a spike-and-slab Lasso prior, we transform the Bayesian variable selection framework of the RMST regression model to a penalized logistic regression problem with a proper choice of the link function. The proposed work can be treated as a more generalized variable selection method comparing to either Cox model or AFT model when the true model is misspecified.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-12-01","The student, Wenjing Yin, accepted the attached license on 2020-11-24 at 13:32.","The student, Wenjing Yin, submitted this Dissertation for approval on 2020-11-24 at 13:52.","This Dissertation was approved for publication on 2020-11-30 at 16:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15954 on 2021-03-04 at 16:19:52","Made available in DSpace on 2021-03-05T21:42:41Z (GMT). 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