Estimation of the signal subspace without estimation of the inverse covariance matrix
dc.contributor.author | Panov, Vladimir | |
dc.date.accessioned | 2017-06-16T00:13:29Z | |
dc.date.available | 2017-06-16T00:13:29Z | |
dc.date.created | 2010-10-27 | |
dc.date.issued | 2010-10-14 | |
dc.identifier.issn | 1860-5664 | |
dc.identifier.uri | http://edoc.hu-berlin.de/18452/4930 | |
dc.description.abstract | Let 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.iso | eng | |
dc.publisher | Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | dimension reduction | eng |
dc.subject | non-Gaussian components | eng |
dc.subject | NGCA | eng |
dc.subject.ddc | 330 Wirtschaft | |
dc.title | Estimation of the signal subspace without estimation of the inverse covariance matrix | |
dc.type | workingPaper | |
dc.identifier.urn | urn:nbn:de:kobv:11-100176492 | |
dc.identifier.doi | http://dx.doi.org/10.18452/4278 | |
local.edoc.pages | 17 | |
local.edoc.type-name | Diskussionspapier | |
local.edoc.container-type | series | |
local.edoc.container-type-name | Schriftenreihe | |
local.edoc.container-year | 2010 | |
dc.identifier.zdb | 2195055-6 | |
bua.series.name | Sonderforschungsbereich 649: Ökonomisches Risiko | |
bua.series.issuenumber | 2010,50 |