The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Tests for High-Dimensional Covariance Matrices
Abstract
We propose tests for sphericity and identity of high-dimensional covariance matrices. The tests are nonparametric without assuming a specific parametric distribution for the data. They can accommodate situations where the data dimension is much larger than the sample size, namely the “large p, small n” situations. We demonstrate by both theoretical and empirical studies that the tests have good properties for a wide range of dimensions and sample sizes. We applied the proposed test on a microarray dataset on Yorkshire Gilts and tested for the covariance structure for the expression levels for sets of genes.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business & Economic Statistics · doi:10.2307/1391399 · Cited by 9,082
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440
On testing the significance of sets of genes
Efron, B., & Tibshirani, R. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas101 · Cited by 798
Higher criticism for detecting sparse heterogeneous mixtures
Donoho, D., & Jin, J. (2004) · The Annals of Statistics · doi:10.1214/009053604000000265 · Cited by 721
A two-sample test for high-dimensional data with applications to gene-set testing
Chen, S. X., & Qin, Y.-L. (2010) · The Annals of Statistics · doi:10.1214/09-aos716 · Cited by 574
Self-citationThe sparsity and bias of the Lasso selection in high-dimensional linear regression
Zhang, C.-H., & Huang, J. (2008) · The Annals of Statistics · doi:10.1214/07-aos520 · Cited by 506
Ledoit, O., & Wolf, M. (2002) · The Annals of Statistics · doi:10.1214/aos/1031689018 · Cited by 399
Barry, W. T., Nobel, A. B., & Wright, F. A. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti260 · Cited by 356
Metadata
- Authors
- Chen, S. X., Zhang, L.-X., & Zhong, P.-S.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2010-06
- Volume
- 105
- Issue
- 490
- Pages
- 810-819
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1990137378
- Cited by
- 329
- References
- 18
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- closed
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