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High-dimensional testing for proportional covariance matrices.
- Source :
-
Journal of Multivariate Analysis . May2019, Vol. 171, p412-420. 9p. - Publication Year :
- 2019
-
Abstract
- Abstract Hypothesis testing for the proportionality of covariance matrices is a classical statistical problem and has been widely studied in the literature. However, there have been few treatments of this test in high-dimensional settings, especially for the case where the number of variables is larger than the sample size, despite high-dimensional statistical inference having recently received considerable attention. This paper studies hypothesis testing for the proportionality of two covariance matrices in the high-dimensional setting: m , n ≍ p δ for some δ ∈ (1 ∕ 2 , 1) , where m and n denote the sample sizes and p denotes the number of variables. A test statistic is proposed and its asymptotic distribution is derived under multivariate normality. The non-asymptotic performance of the proposed test procedure is numerically examined. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0047259X
- Volume :
- 171
- Database :
- Academic Search Index
- Journal :
- Journal of Multivariate Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 134987833
- Full Text :
- https://doi.org/10.1016/j.jmva.2019.01.011