Optimal rates for independence testing via U-statistic permutation tests

Abstract

We study the problem of independence testing given independent and identically distributed pairs taking values in a -finite, separable measure space. Defining a natural measure of dependence D(f) as the squared L^2-distance between a joint density f and the product of its marginals, we first show that there is no valid test of independence that is uniformly consistent against alternatives of the form : D(f) ^2 \. We therefore restrict attention to alternatives that impose additional Sobolev-type smoothness constraints, and define a permutation test based on a basis expansion and a U-statistic estimator of D(f) that we prove is minimax optimal in terms of its separation rates in many instances. Finally, for the case of a Fourier basis on [0,1]^2, we provide an approximation to the power function that offers several additional insights. Our methodology is implemented in the R package USP.

Tasks

Reproductions