Bridge regression: Adaptivity and group selection

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dc.contributor.authorPark, Cheolwooko
dc.contributor.authorYoon, Young Jooko
dc.date.accessioned2021-06-11T01:30:37Z-
dc.date.available2021-06-11T01:30:37Z-
dc.date.created2021-06-11-
dc.date.created2021-06-11-
dc.date.issued2011-11-
dc.identifier.citationJOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.141, no.11, pp.3506 - 3519-
dc.identifier.issn0378-3758-
dc.identifier.urihttp://hdl.handle.net/10203/285759-
dc.description.abstractIn high-dimensional regression problems regularization methods have been a popular choice to address variable selection and multicollinearity. In this paper we study bridge regression that adaptively selects the penalty order from data and produces flexible solutions in various settings. We implement bridge regression based on the local linear and quadratic approximations to circumvent the nonconvex optimization problem. Our numerical study shows that the proposed bridge estimators are a robust choice in various circumstances compared to other penalized regression methods such as the ridge, lasso, and elastic net. In addition, we propose group bridge estimators that select grouped variables and study their asymptotic properties when the number of covariates increases along with the sample size. These estimators are also applied to varying-coefficient models. Numerical examples show superior performances of the proposed group bridge estimators in comparisons with other existing methods. (C) 2011 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.titleBridge regression: Adaptivity and group selection-
dc.typeArticle-
dc.identifier.wosid000293433400013-
dc.identifier.scopusid2-s2.0-80955178309-
dc.type.rimsART-
dc.citation.volume141-
dc.citation.issue11-
dc.citation.beginningpage3506-
dc.citation.endingpage3519-
dc.citation.publicationnameJOURNAL OF STATISTICAL PLANNING AND INFERENCE-
dc.identifier.doi10.1016/j.jspi.2011.05.004-
dc.contributor.localauthorPark, Cheolwoo-
dc.contributor.nonIdAuthorYoon, Young Joo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAnalysis of variance-
dc.subject.keywordAuthorBridge regression-
dc.subject.keywordAuthorMulticollinearity-
dc.subject.keywordAuthorOracle property-
dc.subject.keywordAuthorPenalized regression-
dc.subject.keywordAuthorVariable selection-
dc.subject.keywordAuthorVarying-coefficient models-
dc.subject.keywordPlusVARYING-COEFFICIENT MODELS-
dc.subject.keywordPlusNONCONCAVE PENALIZED LIKELIHOOD-
dc.subject.keywordPlusVARIABLE SELECTION-
dc.subject.keywordPlusGROUP LASSO-
dc.subject.keywordPlusASYMPTOTIC PROPERTIES-
dc.subject.keywordPlusORACLE PROPERTIES-
dc.subject.keywordPlusELASTIC-NET-
dc.subject.keywordPlusESTIMATORS-
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