Testing independence in high-dimensional multivariate normal data

D. Najarzadeh

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 14, 3421-3435

Abstract: A simple statistic was proposed for testing the complete independence of components of random vector having a multivariate normal distribution. Using the martingale central limit theorem, it was proved that this test statistic converges in distribution to the normal as long as both the sample size and dimension go to infinity. Simulations were also carried out to performance evaluation of the proposed test for dealing with the high-dimensional data and the results were compared to those of the state-of-the-art tests. The results confirmed that, in terms of the average relative error criterion, the proposed test has adequate type I error rate control and comparable power to those tests with smaller size distortions.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1702699 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:50:y:2021:i:14:p:3421-3435

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2019.1702699

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().