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dc.contributor.author소병수-
dc.contributor.author오만숙-
dc.date.accessioned2016-08-29T12:08:45Z-
dc.date.available2016-08-29T12:08:45Z-
dc.date.issued2016-
dc.identifier.issn0167-7152-
dc.identifier.otherOAK-19125-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/231803-
dc.description.abstractWe propose a simple Bayesian variable selection method in binary quantile regression. Our method computes the Bayes factors of all candidate models simultaneously based on a single set of MCMC samples from a model that encompasses all candidate models. The method deals with multicollinearity problems and variable selection under constraints. © 2016-
dc.languageEnglish-
dc.publisherElsevier B.V.-
dc.subjectBayes factor-
dc.subjectBayesian model selection-
dc.subjectMarkov chain Monte Carlo-
dc.subjectQuantile regression-
dc.titleBayesian variable selection in binary quantile regression-
dc.typeArticle-
dc.relation.volume118-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage177-
dc.relation.lastpage181-
dc.relation.journaltitleStatistics and Probability Letters-
dc.identifier.doi10.1016/j.spl.2016.07.001-
dc.identifier.wosidWOS:000381839500026-
dc.identifier.scopusid2-s2.0-84979633670-
dc.author.googleOh M.-S.-
dc.author.googlePark E.S.-
dc.author.googleSo B.-S.-
dc.contributor.scopusid소병수(7005199584)-
dc.contributor.scopusid오만숙(7201600334)-
dc.date.modifydate20210929143409-
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자연과학대학 > 통계학전공 > Journal papers
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