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TEST FOR HIGH DIMENSIONAL CORRELATION MATRICES.

ABSTRACT: Testing correlation structures has attracted extensive attention in the literature due to both its importance in real applications and several major theoretical challenges. The aim of this paper is to develop a general framework of testing correlation structures for the one-, two-, and multiple sample testing problems under a high-dimensional setting when both the sample size and data dimension go to infinity. Our test statistics are designed to deal with both the dense and sparse alternatives. We systematically investigate the asymptotic null distribution, power function, and unbiasedness of each test statistic. Theoretically, we make great efforts to deal with the non-independency of all random matrices of the sample correlation matrices. We use simulation studies and real data analysis to illustrate the versatility and practicability of our test statistics.

SUBMITTER: Zheng S

PROVIDER: S-EPMC6709985 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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TEST FOR HIGH DIMENSIONAL CORRELATION MATRICES.

Zheng Shurong S Cheng Guanghui G Guo Jianhua J Zhu Hongtu H

Annals of statistics 20190803 5

Testing correlation structures has attracted extensive attention in the literature due to both its importance in real applications and several major theoretical challenges. The aim of this paper is to develop a general framework of testing correlation structures for the one-, two-, and multiple sample testing problems under a high-dimensional setting when both the sample size and data dimension go to infinity. Our test statistics are designed to deal with both the dense and sparse alternatives. ...[more]

PMID: 31452551

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