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Wishartness and independence of matrix quadratic forms in a normal random matrix

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  • Hu, Jianhua

Abstract

Let Y be an nxp multivariate normal random matrix with general covariance [Sigma]Y. The general covariance [Sigma]Y of Y means that the collection of all np elements in Y has an arbitrary npxnp covariance matrix. A set of general, succinct and verifiable necessary and sufficient conditions is established for matrix quadratic forms Y'WiY's with the symmetric Wi's to be an independent family of random matrices distributed as Wishart distributions. Moreover, a set of general necessary and sufficient conditions is obtained for matrix quadratic forms Y'WiY's to be an independent family of random matrices distributed as noncentral Wishart distributions. Some usual versions of Cochran's theorem are presented as the special cases of these results.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jmvana:v:99:y:2008:i:3:p:555-571
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    References listed on IDEAS

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    1. Mathew, Thomas, 1989. "MANOVA in the multivariate components of variance model," Journal of Multivariate Analysis, Elsevier, pages 30-38.
    2. Wong, C. S. & Wang, T. H., 1993. "Multivariate Versions of Cochran's Theorems II," Journal of Multivariate Analysis, Elsevier, pages 146-159.
    3. Wong, Chi Song & Masaro, Joe & Wang, Tonghui, 1991. "Multivariate versions of Cochran's theorems," Journal of Multivariate Analysis, Elsevier, pages 154-174.
    4. Mathew, Thomas & Nordström, Kenneth, 1997. "Wishart and Chi-Square Distributions Associated with Matrix Quadratic Forms," Journal of Multivariate Analysis, Elsevier, pages 129-143.
    5. Masaro, Joe & Wong, Chi Song, 2003. "Wishart distributions associated with matrix quadratic forms," Journal of Multivariate Analysis, Elsevier, pages 1-9.
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    Cited by:

    1. Hu, Jianhua & Liu, Fuxiang & Ahmed, S. Ejaz, 2012. "Estimation of parameters in the growth curve model via an outer product least squares approach for covariance," Journal of Multivariate Analysis, Elsevier, pages 53-66.

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