Appoximation-assisted [sic] estimation of eigenvectors under quadratic loss

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dc.contributor.authorAhmed, S.E.
dc.date.accessioned2013-05-15T03:53:17Z
dc.date.available2013-05-15T03:53:17Z
dc.date.issued2005
dc.description.abstractImproved estimation of eigen vector of covariance matrix is considered under uncertain prior information (UPI) regarding the parameter vector. Like statistical models underlying the statistical inferences to be made, the prior information will be susceptible to uncertainty and the practitioners may be reluctant to impose the additional information regarding parameters in the estimation process. A very large gain in precision may be achieved by judiciously exploiting the information about the parameters which in practice will be available in any realistic problem. Several estimators based on preliminary test and the Stein-type shrinkage rules are constructed. The expressions for the bias and risk of the proposed estimators are derived and compared with the usual estimators. We demonstrate that how the classical large sample theory of the conventional estimator can be extended to shrinkage and preliminary test estimators for the eigenvector of a covariance matrix. It is established that shrinkage estimators are asymptotically superior to the usual sample estimators. For illustration purposes, the method is applied to three datasets.en
dc.identifier.citationAhmed, S.E. (2005), Appoximation-assisted estimation of eigenvectors under quadratic loss, Research Letters in the Information and Mathematical Sciences, 8, 77-96en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4453
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectEigenvectoren
dc.subjectPrincipal component procedureen
dc.subjectEstimation processen
dc.titleAppoximation-assisted [sic] estimation of eigenvectors under quadratic lossen
dc.typeArticleen
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