{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T11:42:44Z","timestamp":1791546164449,"version":"4.3.4"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2680,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009226","name":"National Security Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In multiple regression problems when covariates can be naturally grouped, it is important to carry out feature selection at the group and within-group individual variable levels simultaneously. The existing methods, including the lasso and group lasso, are designed for either variable selection or group selection, but not for both. We propose a group bridge approach that is capable of simultaneous selection at both the group and within-group individual variable levels. The proposed approach is a penalized regularization method that uses a specially designed group bridge penalty. It has the oracle group selection property, in that it can correctly select important groups with probability converging to one. In contrast, the group lasso and group least angle regression methods in general do not possess such an oracle property in group selection. Simulation studies indicate that the group bridge has superior performance in group and individual variable selection relative to several existing methods.<\/jats:p>","DOI":"10.1093\/biomet\/asp020","type":"journal-article","created":{"date-parts":[[2009,5,20]],"date-time":"2009-05-20T22:01:47Z","timestamp":1242856907000},"page":"339-355","source":"Crossref","is-referenced-by-count":257,"title":["A group bridge approach for variable selection"],"prefix":"10.1093","volume":"96","author":[{"given":"Jian","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science,University of Iowa,221 Schaeffer Hall, Iowa City, Iowa 52242,U.S.A.jian-huang@uiowa.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuange","family":"Ma","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Epidemiology and Public Health,Yale University,New Haven, Connecticut 06520,U.S.A.shuangge.ma@yale.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiliang","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Management Science,University of Miami,Coral Gables, Florida 33124,U.S.A.hxie@exchange.sba.miami.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cun-Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics,Rutgers University,Piscataway, New Jersey 08854,U.S.A.cunhui@stat.rutgers.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2009,6,1]]},"reference":[{"key":"2021061508293296500_R1","first-page":"267","article-title":"Information theory and an extension of the maximum likelihood principle","volume-title":"Second International Symposium on Information Theory","author":"Akaike","year":"1973"},{"key":"2021061508293296500_R2","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression (with Discussion)","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. 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