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

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

Journal of Multivariate Analysis, 2011, vol. 102, issue 10, 1388-1398

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.

Keywords: Order; selection; Cross-validation; Large; sample; covariance; matrices; High-dimensional; data; Marcenko-Pastur; distribution (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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