The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities

Information and Inference: A Journal of the IMA, pp. 2-20, 2011.

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Abstract:

Covariance estimation becomes challenging in the regime where the number p of variables outstrips the number n of samples available to construct the estimate. One way to circumvent this problem is to assume that the covariance matrix is nearly sparse and to focus on estimating only the significant entries. To analyse this approach, Levina...More

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