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Building on a recent framework for distributionally robust optimization, we consider estimation of the inverse covariance matrix for multivariate data.
Optimal uncertainty size in distributionally robust inverse covariance estimation
J. Blanchet and N. Si · 1901
Earlier work this paper cites.
Estimation with quadratic loss
W. James and C. Stein · 1961
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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