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

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. [ABSTRACT FROM AUTHOR]