Matrix means and a novel high-dimensional shrinkage phenomenon-Reference-Cited by-同舟云学术

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Matrix means and a novel high-dimensional shrinkage phenomenon

Published:2022-11-01 Issue:4 Volume:28 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Lodhia Asad¹,Levin Keith²,Levina Elizaveta³

Affiliation:

1. Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142-1027

2. Department of Statistics, University of Wisconsin-Madison, 1220 Medical Sciences Center, 1300 University Ave, Madison, WI 53706-1510

3. Department of Statistics, University of Michigan, 323 West Hall, 1085 S. University Ave, Ann Arbor, MI 48109-1107

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference61 articles.

1. Daniels, M.J. and Kass, R.E. (2001). Shrinkage estimators for covariance matrices. Biometrics 57 1173–1184. 10.1111/j.0006-341X.2001.01173.x

2. Dey, D.K. and Srinivasan, C. (1985). Estimation of a covariance matrix under Stein’s loss. Ann. Statist. 13 1581–1591. 10.1214/aos/1176349756

3. Donoho, D., Gavish, M. and Johnstone, I. (2018). Optimal shrinkage of eigenvalues in the spiked covariance model. Ann. Statist. 46 1742–1778. 10.1214/17-AOS1601

4. Lam, C. (2016). Nonparametric eigenvalue-regularized precision or covariance matrix estimator. Ann. Statist. 44 928–953. 10.1214/15-AOS1393

5. Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 365–411. 10.1016/S0047-259X(03)00096-4

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