Adaptive Testing for High-Dimensional Data

In this article, we propose a class of L-q -norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficients, and testing of component-wise independence for high-dimensional observations, among others. Under the null hypothesis, we derive asymptotic normality and independence between L-q -norm based U-statistics for several qs under mild moment and cumulant conditions. A simple combination of two studentized L-q -based test statistics via their p-values is proposed and is shown to attain great power against alternatives of different sparsity. Our work is a substantial extension of He et al., which is mostly focused on mean and covariance testing, and we manage to provide a general treatment of asymptotic independence of L-q -norm based U-statistics for a wide class of kernels. To alleviate the computation burden, we introduce a variant of the proposed U-statistics by using the monotone indices in the summation, resulting in a U-statistic with asymmetric kernel. A dynamic programming method is introduced to reduce the computational cost from O(n(qr)) , which is required for the calculation of the full U-statistic, to O(n (R)) where r is the order of the kernel. Numerical results further corroborate the advantage of the proposed adaptive test as compared to some existing competitors. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.