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Distribution and characteristic functions for correlated complex Wishart matrices


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  • Smith, Peter J.
  • Garth, Lee M.
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    Let A(t) be a complex Wishart process defined in terms of the MxN complex Gaussian matrix X(t) by A(t)=X(t)X(t)H. The covariance matrix of the columns of X(t) is [Sigma]. If X(t), the underlying Gaussian process, is a correlated process over time, then we have dependence between samples of the Wishart process. In this paper, we study the joint statistics of the Wishart process at two points in time, t1, t2, where t1

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 98 (2007)
    Issue (Month): 4 (April)
    Pages: 661-677

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    Handle: RePEc:eee:jmvana:v:98:y:2007:i:4:p:661-677

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    Keywords: Correlated Wishart Non-central distribution Eigenvalues Hypergeometric function;


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    1. Smith, Peter J. & Gao, Hongsheng, 2000. "A Determinant Representation for the Distribution of a Generalised Quadratic Form in Complex Normal Vectors," Journal of Multivariate Analysis, Elsevier, vol. 73(1), pages 41-54, April.
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