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2010-05-11Buch DOI: 10.18452/4254
Non-Gaussian Component Analysis
dc.contributor.authorPanov, Vladimir
dc.date.accessioned2017-06-16T00:08:27Z
dc.date.available2017-06-16T00:08:27Z
dc.date.created2010-05-27
dc.date.issued2010-05-11
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4906
dc.description.abstractIn this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector X can be represented as a sum of two components - a lowdimensional signal S and a noise component N. We show that this assumption enables us for a special representation for the density function of X. Similar facts are proven in original papers about NGCA ([1], [5], [13]), but our representation differs from the previous versions. The new form helps us to provide a strong theoretical support for the algorithm; moreover, it gives some ideas about new approaches in multidimensional statistical analysis. In this paper, we establish important results for the NGCA procedure using the new representation, and show benefits of our method.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectdimension reductioneng
dc.subjectnon-Gaussian componentseng
dc.subjectEDR subspaceeng
dc.subjectclassification problemeng
dc.subjectValue at Riskeng
dc.subject.ddc330 Wirtschaft
dc.titleNon-Gaussian Component Analysis
dc.typebook
dc.subtitleNew Ideas, New Proofs, New Applications
dc.identifier.urnurn:nbn:de:kobv:11-100111393
dc.identifier.doihttp://dx.doi.org/10.18452/4254
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages23
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2010
local.edoc.container-issue26
local.edoc.container-year2010
local.edoc.container-erstkatid2195055-6

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