文摘
This paper proposes a new test for covariance matrices based on the correction to Rao’s score test in a large-dimension framework. By generalizing the corresponding CLT for linear spectral statistics, the test can be made applicable for large-dimension non-Gaussian variables in a wider range without the 4th-moment restriction. Moreover, the proposed corrected Rao’s score test (CRST) remains powerful even when 62d8ba91307f0a7b5746b04e7" title="Click to view the MathML source">p≫n, which breaks the inherent idea that the corrected tests by RMT can only be used when p<n. Finally, we compare the proposed test with other high-dimension covariance structure tests to evaluate their performances through a simulation study.