A hypothesis test for independence of sets of variates in high dimensions

Zhengyan Lin and Yanbiao Xiang

Statistics & Probability Letters, 2008, vol. 78, issue 17, 2939-2946

Abstract: Srivastava [Srivastava, M.S., 2005. Some tests concerning the covariance matrix in high dimensional data. J. Japan Statist. Soc. 35, 251-272] has proposed a test statistic for testing the hypothesis that the covariance matrix of the normal population is a diagonal matrix when the sample size is smaller than the dimensionality of the data. We extend his results to the hypothesis testing problem for independence of sets of variates in high dimensions. A test statistic is proposed and its asymptotic null distribution is also given, as both the sample size and the number of variables go to infinity. Consequently, this test can be used when the number of variables is not small relative to the sample size, in particular, even when the number of variables exceeds the sample size. Simulations are performed to see the accuracy of the asymptotic null distribution.

Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(08)00240-X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:78:y:2008:i:17:p:2939-2946

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().