Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44044
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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorJiang, Ben_US
dc.creatorLeng, Cen_US
dc.date.accessioned2016-06-07T06:37:46Z-
dc.date.available2016-06-07T06:37:46Z-
dc.identifier.issn0167-7152en_US
dc.identifier.urihttp://hdl.handle.net/10397/44044-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2015 Elsevier B.V. All rights reserved.en_US
dc.rights© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Jiang, B., & Leng, C. (2016). High dimensional discrimination analysis via a semiparametric model. Statistics & Probability Letters, 110, 103-110 is available at https://doi.org/10.1016/j.spl.2015.11.012en_US
dc.subjectBayes ruleen_US
dc.subjectLinear discrimination analysisen_US
dc.subjectMonotone transformationen_US
dc.subjectSemiparametric discriminant analysisen_US
dc.subjectSparsityen_US
dc.titleHigh dimensional discrimination analysis via a semiparametric modelen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage103en_US
dc.identifier.epage110en_US
dc.identifier.volume110en_US
dc.identifier.doi10.1016/j.spl.2015.11.012en_US
dcterms.abstractWe propose a semiparametric linear programming discriminant (SLPD) rule for high dimensional discriminant analysis under a semiparametric model. As an extension, we further propose a two-stage SLPD (TSLPD) rule, which can have better classification performance under mild sparsity assumptions.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationStatistics and probability letters, Mar. 2016, v. 110, p. 103-110en_US
dcterms.isPartOfStatistics and probability lettersen_US
dcterms.issued2016-03-
dc.identifier.isiWOS:000374627200013-
dc.identifier.scopus2-s2.0-84951759751-
dc.identifier.rosgroupid2015000343-
dc.description.ros2015-2016 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAMA-0589-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6602734-
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