Sampling without replacement from a high-dimensional finite population-Reference-Cited by-同舟云学术

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Sampling without replacement from a high-dimensional finite population

Published:2023-11-01 Issue:4 Volume:29 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Hu Jiang¹,Wang Shaochen²,Zhang Yangchun³,Zhou Wang⁴

Affiliation:

1. KLASMOE and School of Mathematics & Statistics, Northeast Normal University, Changchun, 130024, P.R. China

2. School of Mathematics, South China University of Technology, Guangzhou, 510640, P.R. China

3. Department of Mathematics, Shanghai University, Shanghai, 200444, P.R. China

4. Department of Statistics and Data Science, National University of Singapore, 117546, Singapore

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference32 articles.

1. Bai, Z. and Silverstein, J.W. (2010). Spectral Analysis of Large Dimensional Random Matrices, 2nd ed. Springer Series in Statistics. New York: Springer. 10.1007/978-1-4419-0661-8

2. Gao, J., Han, X., Pan, G. and Yang, Y. (2017). High dimensional correlation matrices: The central limit theorem and its applications. J. R. Stat. Soc. Ser. B. Stat. Methodol. 79 677–693. 10.1111/rssb.12189

3. Hu, J., Li, W., Liu, Z. and Zhou, W. (2019). High-dimensional covariance matrices in elliptical distributions with application to spherical test. Ann. Statist. 47 527–555. 10.1214/18-AOS1699

4. Johnstone, I.M. (2001). On the distribution of the largest eigenvalue in principal components analysis. Ann. Statist. 29 295–327. 10.1214/aos/1009210544

5. Silverstein, J.W. and Choi, S.-I. (1995). Analysis of the limiting spectral distribution of large-dimensional random matrices. J. Multivariate Anal. 54 295–309. 10.1006/jmva.1995.1058

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