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On a model selection problem from high-dimensional sample covariance matrices

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  • Chen, J.
  • Delyon, B.
  • Yao, J.-F.

Abstract

Modern random matrix theory indicates that when the population size p is not negligible with respect to the sample size n, the sample covariance matrices demonstrate significant deviations from the population covariance matrices. In order to recover the characteristics of the population covariance matrices from the observed sample covariance matrices, several recent solutions are proposed when the order of the underlying population spectral distribution is known. In this paper, we deal with the underlying order selection problem and propose a solution based on the cross-validation principle. We prove the consistency of the proposed procedure.

Suggested Citation

  • Chen, J. & Delyon, B. & Yao, J.-F., 2011. "On a model selection problem from high-dimensional sample covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 102(10), pages 1388-1398, November.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:10:p:1388-1398
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    References listed on IDEAS

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    1. Silverstein, J. W. & Choi, S. I., 1995. "Analysis of the Limiting Spectral Distribution of Large Dimensional Random Matrices," Journal of Multivariate Analysis, Elsevier, vol. 54(2), pages 295-309, August.
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    1. Ledoit, Olivier & Wolf, Michael, 2017. "Numerical implementation of the QuEST function," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 199-223.

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