Asymptotic Distribution of Wishart Matrix for Block-wise Dispersion of Population Eigenvalues

dc.creatorSheena, Yo
dc.creatorTakemura, Akimichi
dc.date2006-09-11
dc.date2006-09-11
dc.date.accessioned2026-07-07T09:30:17Z
dc.date.available2026-07-07T09:30:17Z
dc.descriptionThis paper deals with the asymptotic distribution of Wishart matrix and its application to the estimation of the population matrix parameter when the population eigenvalues are block-wise infinitely dispersed. We show that the appropriately normalized eigenvectors and eigenvalues asymptotically generate two Wishart matrices and one normally distributed random matrix, which are mutually independent. For a family of orthogonally equivariant estimators, we calculate the asymptotic risks with respect to the entropy or the quadratic loss function and derive the asymptotically best estimator among the family. We numerically show 1) the convergence in both the distributions and the risks are quick enough for a practical use, 2) the asymptotically best estimator is robust against the deviation of the population eigenvalues from the block-wise infinite dispersion.
dc.identifierhttps://arxiv.org/abs/math/0609275
dc.identifierhttp://arxiv.org/abs/math/0609275
dc.identifierJournal of Multivariate Analysis, Vol.99, 2008, 751-775
dc.identifierdoi:10.1016/j.jmva.2007.04.001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/158074
dc.subjectStatistics Theory
dc.subject62H10
dc.titleAsymptotic Distribution of Wishart Matrix for Block-wise Dispersion of Population Eigenvalues
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