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Limiting spectral distribution for a type of sample covariance matrices

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  • Junshan Xie

    (Zhejiang University
    Henan University)

Abstract

This paper focuses on the limiting spectral distribution of the sample covariance matrices with information plus noise type data, which is interesting in the area of array signal processing. Assume that the noise data comes from a multivariate population with an isotropic and log-concave probability law. It is shown that in probability, the empirical spectral distribution converges weakly to a non-random probability distribution, whose Stieltjes transform satisfies a certain equation.

Suggested Citation

  • Junshan Xie, 2013. "Limiting spectral distribution for a type of sample covariance matrices," Indian Journal of Pure and Applied Mathematics, Springer, vol. 44(5), pages 695-710, October.
    • Handle: RePEc:spr:indpam:v:44:y:2013:i:5:d:10.1007_s13226-013-0037-4
    DOI: 10.1007/s13226-013-0037-4
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    References listed on IDEAS

    as
    1. Silverstein, J. W., 1995. "Strong Convergence of the Empirical Distribution of Eigenvalues of Large Dimensional Random Matrices," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 331-339, November.
    2. Silverstein, J. W. & Bai, Z. D., 1995. "On the Empirical Distribution of Eigenvalues of a Class of Large Dimensional Random Matrices," Journal of Multivariate Analysis, Elsevier, vol. 54(2), pages 175-192, August.
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