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Multivariate Versions of Cochran's Theorems II

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  • Wong, C. S.
  • Wang, T. H.

Abstract

A general easily checkable Cochran theorem is obtained for a normal random operator Y. This result does not require that the covariance, [Sigma]Y, of Y is nonsingular or is of the usual form A [circle times operator] [Sigma] ; nor does it assume that the mean, [mu], of Y is equal to zero. Indeed, {Y'WiY} (with nonnegative definite Wi's) is a family of independent Wishart random operators Y'WiY of parameter (mi, [Sigma], [lambda]i) if and only if for some nonnegative definite A and for all i [not equal to] j: (a)(Wi [circle times operator] I)([Sigma]Y - A [circle times operator] [Sigma])(Wi [circle times operator] I) = 0; (b) AWiAWi = AWi, r(AWi) = mi, (c) [lambda]i = [mu]'Wi[mu] = [mu]'WiAWi[mu]; and (d) (Wi [circle times operator] I)[Sigma]Y(Wj [circle times operator] I) = 0. The usual multivariate versions of Cochran's theorem are contained in a special case of our result where [Sigma]Y = A [circle times operator] [Sigma]. The A in our version of Cochran's theorem can actually be constructed from [Sigma], [Sigma]Y, and the sum of the Wi's.

Suggested Citation

  • Wong, C. S. & Wang, T. H., 1993. "Multivariate Versions of Cochran's Theorems II," Journal of Multivariate Analysis, Elsevier, vol. 44(1), pages 146-159, January.
  • Handle: RePEc:eee:jmvana:v:44:y:1993:i:1:p:146-159
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    Cited by:

    1. Mathew, Thomas & Nordström, Kenneth, 1997. "Wishart and Chi-Square Distributions Associated with Matrix Quadratic Forms," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 129-143, April.
    2. Masaro, Joe & Wong, Chi Song, 2003. "Wishart distributions associated with matrix quadratic forms," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 1-9, April.
    3. Mortarino, Cinzia, 2005. "A decomposition for a stochastic matrix with an application to MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 134-144, January.
    4. Hu, Jianhua, 2008. "Wishartness and independence of matrix quadratic forms in a normal random matrix," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 555-571, March.
    5. Masaro, Joe & Wong, Chi Song, 2010. "Wishart-Laplace distributions associated with matrix quadratic forms," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1168-1178, May.
    6. Phil D. Young & Joshua D. Patrick & Dean M. Young, 2023. "A Brief Derivation of Necessary and Sufficient Conditions for a Family of Matrix Quadratic Forms to Have Mutually Independent Non-Central Wishart Distributions," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 478-484, February.

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