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Article: Power computation for hypothesis testing with high-dimensional covariance matrices

TitlePower computation for hypothesis testing with high-dimensional covariance matrices
Authors
Issue Date2016
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics & Data Analysis, 2016, v. 104, p. 10-23 How to Cite?
Persistent Identifierhttp://hdl.handle.net/10722/231164
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283

 

DC FieldValueLanguage
dc.contributor.authorLIN, R-
dc.contributor.authorZheng, S-
dc.contributor.authorLiu, Z-
dc.contributor.authorYin, G-
dc.date.accessioned2016-09-20T05:21:05Z-
dc.date.available2016-09-20T05:21:05Z-
dc.date.issued2016-
dc.identifier.citationComputational Statistics & Data Analysis, 2016, v. 104, p. 10-23-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/10722/231164-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda-
dc.relation.ispartofComputational Statistics & Data Analysis-
dc.rightsPosting accepted manuscript (postprint): © <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.titlePower computation for hypothesis testing with high-dimensional covariance matrices-
dc.typeArticle-
dc.identifier.emailYin, G: gyin@hku.hk-
dc.identifier.authorityYin, G=rp00831-
dc.identifier.doi10.1016/j.csda.2016.05.008-
dc.identifier.hkuros265843-
dc.identifier.volume104-
dc.identifier.spage10-
dc.identifier.epage23-
dc.publisher.placeNetherlands-

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