Optimal Calibration for Multiple Testing against Local Inhomogeneity in Higher Dimension

TL;DR

This paper introduces an exact, adaptive multiple testing procedure based on randomized nearest-neighbor statistics for detecting differences between multivariate distributions without prior density assumptions, achieving sharp optimality.

Contribution

It develops a novel, assumption-free multiple testing method for multivariate densities that is proven to be sharp-optimal and asymptotically adaptive in high-dimensional settings.

Findings

The proposed test is sharp-optimal for typical data arrangements.

It is spatially and sharply asymptotically adaptive for isotropic Hölder classes.

The method achieves exact minimax risk asymptotics.

Abstract

Based on two independent samples X_1,...,X_m and X_{m+1},...,X_n drawn from multivariate distributions with unknown Lebesgue densities p and q respectively, we propose an exact multiple test in order to identify simultaneously regions of significant deviations between p and q. The construction is built from randomized nearest-neighbor statistics. It does not require any preliminary information about the multivariate densities such as compact support, strict positivity or smoothness and shape properties. The properly adjusted multiple testing procedure is shown to be sharp-optimal for typical arrangements of the observation values which appear with probability close to one. The proof relies on a new coupling Bernstein type exponential inequality, reflecting the non-subgaussian tail behavior of a combinatorial process. For power investigation of the proposed method a reparametrized minimax set-up is introduced, reducing the composite hypothesis "p=q" to a simple one with the multivariate mixed density (m/n)p+(1-m/n)q as infinite dimensional nuisance parameter. Within this framework, the test is shown to be spatially and sharply asymptotically adaptive with respect to uniform loss on isotropic H\"older classes. The exact minimax risk asymptotics are obtained in terms of solutions of the optimal recovery.