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dc.contributor.authorNeudecker, Heinz
dc.date.accessioned2007-11-12T19:06:33Z
dc.date.available2007-11-12T19:06:33Z
dc.date.issued2004
dc.identifier.citationNeudecker, Heinz. "On best affine unbiased covariance-preserving prediction of factor scores". SORT, 2004, Vol. 28, núm. 1
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2099/3746
dc.description.abstractThis paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. They examined procedures and results as proposed by Anderson & Rubin, McDonald, Green and Krijnen, Wansbeek & ten Berge. We shall consider the same matter, under weaker rank assumptions. We allow some moments, namely the variance of the observable scores vector and that of the unique factors,to be singular. We require T′ T > 0, where T T′ is a Schur decomposition of. As usual the variance of the common factors, , and the loadings matrix Awill have full column rank.
dc.format.extent27-36
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2004, Vol. 28, Núm. 1 [January-June]
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.subject.otherMultivariate analysis
dc.subject.otherAlgebras, Linear
dc.subject.otherMultilinear algebra
dc.subject.otherMatrices
dc.titleOn best affine unbiased covariance-preserving prediction of factor scores
dc.typeArticle
dc.subject.lemacAnàlisi multivariable
dc.subject.lemacÀlgebra lineal
dc.subject.lemacÀlgebra multilineal
dc.subject.lemacMatriu S, Teoria
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::15 Linear and multilinear algebra; matrix theory
dc.subject.amsClassificació AMS::62 Statistics::62H Multivariate analysis
dc.rights.accessOpen Access
local.personalitzacitaciotrue


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