Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144459
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dc.contributor.authorYang, Guangrenen_US
dc.contributor.authorLiu, Yimingen_US
dc.contributor.authorPan, Guangmingen_US
dc.date.accessioned2020-11-06T03:03:27Z-
dc.date.available2020-11-06T03:03:27Z-
dc.date.issued2019-
dc.identifier.citationYang, G., Liu, Y., & Pan, G. (2019). Weighted covariance matrix estimation. Computational Statistics & Data Analysis, 139, 82–98. doi:10.1016/j.csda.2019.04.017en_US
dc.identifier.issn0167-9473en_US
dc.identifier.urihttps://hdl.handle.net/10356/144459-
dc.description.abstractThe paper proposes a cross-validated linear shrinkage estimation for population covariance matrices. Moreover we also propose a novel weighted estimator based on the thresholding and shrinkage methods for high dimensional datasets. It is applicable to a wider scope of different structures of covariance matrices. Some theoretical results about the cross-validated shrinkage method and weighted covariance estimation methods are also developed. The finite-sample performance of the proposed methods is illustrated through extensive simulations and real data analysis.en_US
dc.language.isoenen_US
dc.relation.ispartofComputational Statistics & Data Analysisen_US
dc.rights© 2019 Elsevier B.V. All rights reserved. This paper was published in Computational Statistics & Data Analysis and is made available with permission of Elsevier B.V.en_US
dc.subjectScience::Mathematicsen_US
dc.titleWeighted covariance matrix estimationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1016/j.csda.2019.04.017-
dc.description.versionAccepted versionen_US
dc.identifier.volume139en_US
dc.identifier.spage82en_US
dc.identifier.epage98en_US
dc.subject.keywordsThresholdingen_US
dc.subject.keywordsShrinkageen_US
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