Identity tests for high dimensional data using RMT

Wang, C; Yang, J; Miao, B; Cao, L

Identity tests for high dimensional data using RMT

Wang, C Yang, J Miao, B Cao, L

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Publication Type:: Journal Article
Citation:: Journal of Multivariate Analysis, 2013, 118 pp. 128 - 137
Issue Date:: 2013-07-01

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Field	Value	Language
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Miao, B	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2013-07-01	en_US
dc.identifier.citation	Journal of Multivariate Analysis, 2013, 118 pp. 128 - 137	en_US
dc.identifier.issn	0047-259X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23744
dc.description.abstract	In this work, we redefined two important statistics, the CLRT test [Z. Bai, D. Jiang, J. Yao, S. Zheng, Corrections to LRT on large-dimensional covariance matrix by RMT, The Annals of Statistics 37 (6B) (2009) 3822-3840] and the LW test [O. Ledoit, M. Wolf, Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size, The Annals of Statistics (2002) 1081-1102] on identity tests for high dimensional data using random matrix theories. Compared with existing CLRT and LW tests, the new tests can accommodate data which has unknown means and non-Gaussian distributions. Simulations demonstrate that the new tests have good properties in terms of size and power. What is more, even for Gaussian data, our new tests perform favorably in comparison to existing tests. Finally, we find the CLRT is more sensitive to eigenvalues less than 1 while the LW test has more advantages in relation to detecting eigenvalues larger than 1. © 2013 Elsevier Inc.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130102691
dc.relation.ispartof	Journal of Multivariate Analysis	en_US
dc.relation.isbasedon	10.1016/j.jmva.2013.03.015	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Identity tests for high dimensional data using RMT	en_US
dc.type	Journal Article
utslib.citation.volume	118	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	118	en_US

Abstract:

In this work, we redefined two important statistics, the CLRT test [Z. Bai, D. Jiang, J. Yao, S. Zheng, Corrections to LRT on large-dimensional covariance matrix by RMT, The Annals of Statistics 37 (6B) (2009) 3822-3840] and the LW test [O. Ledoit, M. Wolf, Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size, The Annals of Statistics (2002) 1081-1102] on identity tests for high dimensional data using random matrix theories. Compared with existing CLRT and LW tests, the new tests can accommodate data which has unknown means and non-Gaussian distributions. Simulations demonstrate that the new tests have good properties in terms of size and power. What is more, even for Gaussian data, our new tests perform favorably in comparison to existing tests. Finally, we find the CLRT is more sensitive to eigenvalues less than 1 while the LW test has more advantages in relation to detecting eigenvalues larger than 1. © 2013 Elsevier Inc.

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http://hdl.handle.net/10453/23744