On the Strong Convergence of the Optimal Linear Shrinkage Estimator for Large Dimensional Covariance Matrix

Abstract

In this work we construct an optimal linear shrinkage estimator for the covariance matrix in high dimensions. The recent results from the random matrix theory allow us to find the asymptotic deterministic equivalents of the optimal shrinkage intensities and estimate them consistently. The developed distribution-free estimators obey almost surely the smallest Frobenius loss over all linear shrinkage estimators for the covariance matrix. The case we consider includes the number of variables $p\to \infty$ and the sample size $n\to \infty$ so that $p/n\to c\in (0,+\infty )$. Additionally, we prove that the Frobenius norm of the sample covariance matrix tends almost surely to a deterministic quantity which can be consistently estimated.

21 pages, 2 figures. arXiv admin note: text overlap with arXiv:1308.0931, revised version (Journal of Multivariate Analysis)

References in corpus (4)

• Adaptive covariance matrix estimation through block thresholding
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