Bootstrapping the operator norm in high dimensions: Error estimation for covariance matrices and sketching-Reference-Cited by-同舟云学术

Bootstrapping the operator norm in high dimensions: Error estimation for covariance matrices and sketching

Published:2023-02-01 Issue:1 Volume:29 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Lopes Miles E.¹,Erichson N. Benjamin²,Mahoney Michael W.²

Affiliation:

1. Department of Statistics, University of California, Davis, Mathematical Sciences Building, Davis, CA, 95616, USA

2. Department of Statistics, University of California, Berkeley, Evans Hall, Berkeley, CA, 94720, USA and International Computer Science Institute, Berkeley, CA, 94704, USA

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference72 articles.

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2. Bickel, P.J. and Levina, E. (2008). Regularized estimation of large covariance matrices. Ann. Statist. 36 199–227. 10.1214/009053607000000758

3. Cai, T.T., Zhang, C.-H. and Zhou, H.H. (2010). Optimal rates of convergence for covariance matrix estimation. Ann. Statist. 38 2118–2144. 10.1214/09-AOS752

4. Chernozhukov, V., Chetverikov, D. and Kato, K. (2014). Gaussian approximation of suprema of empirical processes. Ann. Statist. 42 1564–1597. 10.1214/14-AOS1230

5. Adamczak, R., Litvak, A.E., Pajor, A. and Tomczak-Jaegermann, N. (2011). Sharp bounds on the rate of convergence of the empirical covariance matrix. C. R. Math. Acad. Sci. Paris 349 195–200. 10.1016/j.crma.2010.12.014

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