🤖 AI Summary
This study addresses the challenges of low statistical power and difficulty in level control arising from subtle element-wise differences and sample dependence in testing the equality of high-dimensional covariance matrices. To overcome these issues, this work proposes a studentized L2-type statistic combined with a zero-preserving transformation, introducing a nonsingular linear transformation to incorporate structural information without dimension reduction. Furthermore, it employs a Gaussian multiplier bootstrap to approximate the null distribution, thereby avoiding the construction of the full covariance matrix, and establishes the asymptotic invariance when replacing population transformations with their sample estimates. The contributions include deriving finite-sample Gaussian approximation bounds, demonstrating substantially improved test size accuracy and power under weak dependence, and validating the method's effectiveness through breast cancer gene expression data.
📝 Abstract
Testing equality of two high-dimensional covariance matrices is challenging when many entries differ only slightly. Dependence among sample covariance entries can also affect the finite-sample size and power of tests that aggregate their differences. We propose a studentized $\ell_2$-type statistic that averages squared, marginally standardized differences between corresponding entries of the two sample covariance matrices. Before constructing the statistic, we apply the same nonsingular linear transformation to both samples. In the transformed coordinates, the covariance-equality hypothesis is unchanged, whereas the standardized differences and correlations among their estimators generally change. The transformation can therefore incorporate structural or scientific information without reducing dimension. We approximate the null distribution using a Gaussian multiplier bootstrap that uses coordinatewise studentization and avoids forming or inverting the full covariance matrix of the vectorized sample covariance entries. For deterministic transformations, we derive nonasymptotic Gaussian and bootstrap approximation bounds and establish asymptotic size validity and power consistency. We also show that replacing a population transformation by a same-sample estimator leaves the statistic and bootstrap critical value asymptotically unchanged in relative terms under an operator-norm convergence condition. Simulations show that suitable transformations improve size accuracy and power under weak dependence. An analysis of breast cancer gene-expression data illustrates the method.