Testing identity of high-dimensional covariance matrix. Issue 13 (2nd September 2018)

• Record Type: Journal Article
• Authors: Wang, Hao; Liu, Baisen; Shi, Ning-Zhong; Zheng, Shurong
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 13(2018)
• Page Start: 2600
• Page End: 2611
• Publication Date: 2018-09-02
• Subjects: Identity hypothesis -- high-dimensional covariance matrix -- asymptotic distributions -- large dimensional random matrix theory; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1479410
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Two new statistics are proposed for testing the identity of high-dimensional covariance matrix. Applying the large dimensional random matrix theory, we study the asymptotic distributions of our proposed statistics under the situation that the dimension p and the sample size n tend to infinity proportionally. The proposed tests can accommodate the situation that the data dimension is much larger than the sample size, and the situation that the population distribution is non-Gaussian. The numerical studies demonstrate that the proposed tests have good performance on the empirical powers for a wide range of dimensions and sample sizes.