Wishart and Chi-Square Distributions Associated with Matrix Quadratic Forms
AbstractFor a normally distributed random matrixYwith a general variance-covariance matrix[Sigma]Y, and for a nonnegative definite matrixQ, necessary and sufficient conditions are derived for the Wishartness ofY'QY. The conditions resemble those obtained by Wong, Masaro, and Wang (1991,J. Multivariate Anal.39, 154-174) and Wong and Wang (1993,J. Multivariate Anal.44, 146-159), but are verifiable and are obtained by elementary means. An explicit characterization is also obtained for the structure of[Sigma]Yunder which the distribution ofY'QYis Wishart. Assuming[Sigma]Ypositive definite, a necessary and sufficient condition is derived for every univariate quadratic fromlY'QYlto be distributed as a multiple of a chi-square. For the caseQ=In, the corresponding structure of[Sigma]Yis identified. An explicit counterexample is constructed showing that Wishartness ofY'Yneed not follow when, for every vectorl,Â l'Y'Ylis distributed as a multiple of a chi-square, complementing the well-known counterexample by Mitra (1969,Sankhya31, 19-22). Application of the results to multivariate components of variance models is briefly indicated.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 61 (1997)
Issue (Month): 1 (April)
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