Economics at your fingertips  

Testing block-diagonal covariance structure for high-dimensional data under non-normality

Yuki Yamada, Masashi Hyodo and Takahiro Nishiyama

Journal of Multivariate Analysis, 2017, vol. 155, issue C, 305-316

Abstract: In this article, we propose a test for making an inference about the block-diagonal covariance structure of a covariance matrix in non-normal high-dimensional data. We prove that the limiting null distribution of the proposed test is normal under mild conditions when its dimension is substantially larger than its sample size. We further study the local power of the proposed test. Finally, we study the finite-sample performance of the proposed test via Monte Carlo simulations. We demonstrate the relevance and benefits of the proposed approach for a number of alternative covariance structures.

Keywords: Tests of covariance structure; High dimension; Statistical hypothesis testing; Non-normality (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
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:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:jmvana:v:155:y:2017:i:c:p:305-316