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November 2018 Optimal estimation of a large-dimensional covariance matrix under Stein’s loss
Olivier Ledoit, Michael Wolf
Bernoulli 24(4B): 3791-3832 (November 2018). DOI: 10.3150/17-BEJ979


This paper introduces a new method for deriving covariance matrix estimators that are decision-theoretically optimal within a class of nonlinear shrinkage estimators. The key is to employ large-dimensional asymptotics: the matrix dimension and the sample size go to infinity together, with their ratio converging to a finite, nonzero limit. As the main focus, we apply this method to Stein’s loss. Compared to the estimator of Stein (Estimation of a covariance matrix (1975); J. Math. Sci. 34 (1986) 1373–1403), ours has five theoretical advantages: (1) it asymptotically minimizes the loss itself, instead of an estimator of the expected loss; (2) it does not necessitate post-processing via an ad hoc algorithm (called “isotonization”) to restore the positivity or the ordering of the covariance matrix eigenvalues; (3) it does not ignore any terms in the function to be minimized; (4) it does not require normality; and (5) it is not limited to applications where the sample size exceeds the dimension. In addition to these theoretical advantages, our estimator also improves upon Stein’s estimator in terms of finite-sample performance, as evidenced via extensive Monte Carlo simulations. To further demonstrate the effectiveness of our method, we show that some previously suggested estimators of the covariance matrix and its inverse are decision-theoretically optimal in the large-dimensional asymptotic limit with respect to the Frobenius loss function.


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Olivier Ledoit. Michael Wolf. "Optimal estimation of a large-dimensional covariance matrix under Stein’s loss." Bernoulli 24 (4B) 3791 - 3832, November 2018.


Received: 1 April 2016; Revised: 1 July 2017; Published: November 2018
First available in Project Euclid: 18 April 2018

zbMATH: 06869892
MathSciNet: MR3788189
Digital Object Identifier: 10.3150/17-BEJ979

Rights: Copyright © 2018 Bernoulli Society for Mathematical Statistics and Probability


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Vol.24 • No. 4B • November 2018
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