Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances

AbstractIn this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which is ubiquitous in high-dimensional data. To simultaneously accommodate such heteroscedasticity and high dimensionality, we propose a novel test based on an aggregation of the marginal cumulative covariances, requiring no prior information on the specific form of regression models. Our proposed test statistic is scale-invariance, tuning-free and convenient to implement. The asymptotic normality of the proposed statistic is established under the null hypothesis. We further study the asymptotic relative efficiency of our proposed test with respect to the state-of-art universal tests in two different settings: one is designed for high-dimensional linear model and the other is introduced in a completely model-free setting. A remarkable finding reveals that, thanks to the scale-invariance property, even under the high-dimensional linear models, our proposed test is asymptotically much more powerful than existing competitors for the covariates with heterogeneous variances while maintaining high efficiency for the homoscedastic ones. Supplementary materials for this article are available online.Keywords: Conditional mean independenceCumulative covarianceHigh dimensionMartingale difference divergence Supplementary MaterialsWe provide proofs of Theorems 1–4, fast algorithms for computing the two competitive test statistics, and additional discussions and simulations on the asymptotic relative efficiency in the supplementary material.AcknowledgmentsWe are grateful to the Editor, the Associate Editor and reviewers for their constructive comments, which lead to a significant improvement of this work. We are also very grateful to Professor Min Qian for providing us with the R codes of McKeague and Qian (Citation2015).FundingThis work is supported by National Natural Science Foundation of China (12171477, 11901006, 12001405, 11731011, 11931014), Natural Science Foundation of Anhui Province (1908085QA06) and Natural Science Foundation of Beijing Municipality (Z190002), Fundamental Research Funds for the Central Universities (22120210557), and National Science Foundation (DMS-1820702, 1953196 and 2015539).Additional informationFundingThis work is supported by National Natural Science Foundation of China (12171477, 11901006, 12001405, 11731011, 11931014), Natural Science Foundation of Anhui Province (1908085QA06) and Natural Science Foundation of Beijing Municipality (Z190002), Fundamental Research Funds for the Central Universities (22120210557), and National Science Foundation (DMS-1820702, 1953196 and 2015539).