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2010-10-14Buch DOI: 10.18452/4278
Estimation of the signal subspace without estimation of the inverse covariance matrix
dc.contributor.authorPanov, Vladimir
dc.date.accessioned2017-06-16T00:13:29Z
dc.date.available2017-06-16T00:13:29Z
dc.date.created2010-10-27
dc.date.issued2010-10-14
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4930
dc.description.abstractLet a high-dimensional random vector X can be represented as a sum of two components - a signal S, which belongs to some low-dimensional subspace S, and a noise component N. This paper presents a new approach for estimating the subspace S based on the ideas of the Non-Gaussian Component Analysis. Our approach avoids the technical difficulties that usually exist in similar methods - it doesn’t require neither the estimation of the inverse covariance matrix of X nor the estimation of the covariance matrix of N.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectdimension reductioneng
dc.subjectnon-Gaussian componentseng
dc.subjectNGCAeng
dc.subject.ddc330 Wirtschaft
dc.titleEstimation of the signal subspace without estimation of the inverse covariance matrix
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100176492
dc.identifier.doihttp://dx.doi.org/10.18452/4278
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages17
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2010
local.edoc.container-issue50
local.edoc.container-year2010
local.edoc.container-erstkatid2195055-6

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