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Article: On a model selection problem from high-dimensional sample covariance matrices

TitleOn a model selection problem from high-dimensional sample covariance matrices
Authors
Chen, JDelyon, BYao, JF
KeywordsCross-validation
High-dimensional data
Large sample covariance matrices
Marčenko-Pastur distribution
Order selection
Primary
Secondary
Issue Date2011
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal Of Multivariate Analysis, 2011, v. 102 n. 10, p. 1388-1398 How to Cite?
DOI: http://dx.doi.org/10.1016/j.jmva.2011.05.005
AbstractModern random matrix theory indicates that when the population size p is not negligible with respect to the sample size n, the sample covariance matrices demonstrate significant deviations from the population covariance matrices. In order to recover the characteristics of the population covariance matrices from the observed sample covariance matrices, several recent solutions are proposed when the order of the underlying population spectral distribution is known. In this paper, we deal with the underlying order selection problem and propose a solution based on the cross-validation principle. We prove the consistency of the proposed procedure. © 2011 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/135512
ISSN
0047-259X
2021 Impact Factor: 1.387
2020 SCImago Journal Rankings: 1.283
ISI Accession Number ID
WOS:000293048300006
Funding AgencyGrant Number
Chinese NSF10571020
Region Bretagne, FranceARED 06007848
Funding Information:

The research of Jiaqi Chen was supported by the Chinese NSF grant 10571020 and Research Grant ARED 06007848 from Region Bretagne, France.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChen, Jen_HK
dc.contributor.authorDelyon, Ben_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-07-27T01:36:14Z-
dc.date.available2011-07-27T01:36:14Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Multivariate Analysis, 2011, v. 102 n. 10, p. 1388-1398en_HK
dc.identifier.issn0047-259Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/135512-
dc.description.abstractModern random matrix theory indicates that when the population size p is not negligible with respect to the sample size n, the sample covariance matrices demonstrate significant deviations from the population covariance matrices. In order to recover the characteristics of the population covariance matrices from the observed sample covariance matrices, several recent solutions are proposed when the order of the underlying population spectral distribution is known. In this paper, we deal with the underlying order selection problem and propose a solution based on the cross-validation principle. We prove the consistency of the proposed procedure. © 2011 Elsevier Inc.en_HK
dc.languageengen_US
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmvaen_HK
dc.relation.ispartofJournal of Multivariate Analysisen_HK
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.en_US
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in <Journal of Multivariate Analysis>. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [VOL 102, ISSUE 10, (NOV 2011)] DOI 10.1016/j.jmva.2011.05.005-
dc.subjectCross-validationen_HK
dc.subjectHigh-dimensional dataen_HK
dc.subjectLarge sample covariance matricesen_HK
dc.subjectMarčenko-Pastur distributionen_HK
dc.subjectOrder selectionen_HK
dc.subjectPrimaryen_HK
dc.subjectSecondaryen_HK
dc.titleOn a model selection problem from high-dimensional sample covariance matricesen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.jmva.2011.05.005en_HK
dc.identifier.scopuseid_2-s2.0-79960060259en_HK
dc.identifier.hkuros187967en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960060259&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume102en_HK
dc.identifier.issue10en_HK
dc.identifier.spage1388en_HK
dc.identifier.epage1398en_HK
dc.identifier.isiWOS:000293048300006-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChen, J=38662179100en_HK
dc.identifier.scopusauthoridDelyon, B=6701472779en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK
dc.identifier.citeulike9380819-
dc.identifier.issnl0047-259X-

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