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dc.contributor.author유재근-
dc.date.accessioned2016-08-28T10:08:21Z-
dc.date.available2016-08-28T10:08:21Z-
dc.date.issued2013-
dc.identifier.issn0167-9473-
dc.identifier.otherOAK-9692-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/223371-
dc.description.abstractA seeded dimension reduction approach recently developed provides a paradigm to enable existing dimension reduction methods for the central subspace to be adapted to regressions with n<p. The approach is based on successive projection of a seed matrix on a subspace to contain the central subspace. In the article, we will suggest a bootstrap determination procedure to select a proper value for terminating the projections. Also, extensions of seeded dimension reduction are proposed to cover more various types of regressions with n<p such as a categorical predictor regression and survival regression. Then we apply the new development to analyze diffuse large-B-cell lymphoma data and leukemia data. Numerical studies are also presented. © 2012 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.titleAdvances in seeded dimension reduction: Bootstrap criteria and extensions-
dc.typeArticle-
dc.relation.issue1-
dc.relation.volume60-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage70-
dc.relation.lastpage79-
dc.relation.journaltitleComputational Statistics and Data Analysis-
dc.identifier.doi10.1016/j.csda.2012.10.003-
dc.identifier.wosidWOS:000314137800005-
dc.identifier.scopusid2-s2.0-84871816146-
dc.author.googleYoo J.K.-
dc.contributor.scopusid유재근(23032759600)-
dc.date.modifydate20180302081000-
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자연과학대학 > 통계학전공 > Journal papers
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