A Powerful Bootstrap Test of Independence in High Dimensions

This paper proposes a nonparametric test of independence of one random variable from a large pool of other random variables. The test statistic is the maximum of several Chatterjee's rank correlations and critical values are computed via a block multiplier bootstrap. The test is shown to asymptotically control size uniformly over a large class of data-generating processes, even when the number of variables is much larger than sample size. The test is consistent against any fixed alternative. It can be combined with a stepwise procedure for selecting those variables from the pool that violate independence, while controlling the family-wise error rate. All formal results leave the dependence among variables in the pool completely unrestricted. In simulations, we find that our test is very powerful, outperforming existing tests in most scenarios considered, particularly in high dimensions and/or when the variables in the pool are dependent.