Sharp optimality for high-dimensional covariance testing under sparse signals-Reference-Cited by-同舟云学术

Sharp optimality for high-dimensional covariance testing under sparse signals

Published:2023-10-01 Issue:5 Volume:51 Page:
ISSN:0090-5364
Container-title:The Annals of Statistics
language:
Short-container-title:Ann. Statist.

Author:

Chen Song Xi¹,Qiu Yumou²,Zhang Shuyi³

Affiliation:

1. Guanghua School of Management and Center for Statistical Science, Peking University

2. School of Mathematical Sciences and Center for Statistical Science, Peking University

3. KLATASDS-MoE, School of Statistics, Academy of Statistics and Interdisciplinary Sciences, East China Normal University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference27 articles.

1. Cai, T. T. and Ma, Z. (2013). Optimal hypothesis testing for high dimensional covariance matrices. Bernoulli 19 2359–2388.

2. Bradley, R. C. (2005). Basic properties of strong mixing conditions. A survey and some open questions. Probab. Surv. 2 107–144.

3. Berbee, H. C. P. (1979). Random Walks with Stationary Increments and Renewal Theory. Mathematical Centre Tracts 112. Mathematisch Centrum, Amsterdam.

4. LEDOIT, O. and WOLF, M. (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Statist. 30 1081–1102.

5. NAGAO, H. (1973). On some test criteria for covariance matrix. Ann. Statist. 1 700–709.

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