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Improving the Graphical Lasso Estimation for the Precision Matrix Through Roots of the Sample Covariance Matrix

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorAvagyan, Vahe
dc.contributor.authorAlonso Fernández, Andrés Modesto
dc.contributor.authorNogales Martin, Francisco J.
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-06-25T10:31:42Z
dc.date.available2021-06-25T10:31:42Z
dc.date.issued2017-10-13
dc.description.abstractIn this article, we focus on the estimation of a high-dimensional inverse covariance (i.e., precision) matrix. We propose a simple improvement of the graphical Lasso (glasso) framework that is able to attain better statistical performance without increasing significantly the computational cost. The proposed improvement is based on computing a root of the sample covariance matrix to reduce the spread of the associated eigenvalues. Through extensive numerical results, using both simulated and real datasets, we show that the proposed modification improves the glasso procedure. Our results reveal that the square-root improvement can be a reasonable choice in practice. Supplementary material for this article is available online.en
dc.description.sponsorshipAndrés M. Alonso gratefully acknowledges financial support from CICYT Grants ECO2012-38442 and CO2015-66593. Francisco J. Nogales and Vahe Avagyan were supported by the Spanish Government through project MTM2013-44902-P.en
dc.format.extent8
dc.identifier.bibliographicCitationAvagyan, V., Alonso, A. M. & Nogales, F. J. (2017). Improving the Graphical Lasso Estimation for the Precision Matrix Through Roots of the Sample Covariance Matrix. Journal of Computational and Graphical Statistics, 26(4), pp. 865–872.en
dc.identifier.doihttps://doi.org/10.1080/10618600.2017.1340890
dc.identifier.issn1061-8600
dc.identifier.publicationfirstpage865
dc.identifier.publicationissue4
dc.identifier.publicationlastpage872
dc.identifier.publicationtitleJournal of Computational and Graphical Statisticsen
dc.identifier.publicationvolume26
dc.identifier.urihttps://hdl.handle.net/10016/32936
dc.identifier.uxxiAR/0000020958
dc.language.isoeng
dc.publisherTaylor & Francisen
dc.relation.projectIDGobierno de España. ECO2012-38442es
dc.relation.projectIDGobierno de España. ECO2015-66593-Pes
dc.relation.projectIDGobierno de España. MTM2013-44902-Pes
dc.rights© 2017 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North Americaen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherGaussian graphical modelen
dc.subject.otherGene expressionen
dc.subject.otherHigh-dimensionalityen
dc.subject.otherPenalized estimationen
dc.subject.otherPortfolio selectionen
dc.titleImproving the Graphical Lasso Estimation for the Precision Matrix Through Roots of the Sample Covariance Matrixen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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