Robust Nonparametric Two-Sample Tests via Mutual Information using Extended Bregman Divergence

TL;DR

This paper develops a robust nonparametric two-sample testing framework using a generalized mutual information based on extended Bregman divergence, unifying several divergence measures and demonstrating superior robustness and power.

Contribution

It introduces a new class of nonparametric two-sample tests based on generalized mutual information, unifying multiple divergence measures and analyzing their robustness and efficiency.

Findings

Proposed tests are consistent and asymptotically normal.

Divergences beyond the power divergence family offer better robustness.

The methodology performs well on real data applications.

Abstract

We introduce a generalized formulation of mutual information (MI) based on the extended Bregman divergence, a framework that subsumes the generalized S-Bregman (GSB) divergence family. The GSB divergence unifies two important classes of statistical distances, namely the S-divergence and the Bregman exponential divergence (BED), thereby encompassing several widely used subfamilies, including the power divergence (PD), density power divergence (DPD), and S-Hellinger distance (S-HD). In parametric inference, minimum divergence estimators are well known to balance robustness with high asymptotic efficiency relative to the maximum likelihood estimator. However, nonparametric tests based on such statistical distances have been relatively less explored. In this paper, we construct a class of consistent and robust nonparametric two-sample tests for the equality of two absolutely continuous distributions using the generalized MI. We establish the asymptotic normality of the proposed test statistics under the null and contiguous alternatives. The robustness properties of the generalized MI are rigorously studied through the influence function and the breakdown point, demonstrating that stability of the generalized MI translates into stability of the associated tests. Extensive simulation studies show that divergences beyond the PD family often yield superior robustness under contamination while retaining high asymptotic power. A data-driven scheme for selecting optimal tuning parameters is also proposed. Finally, the methodology is illustrated with applications to real data.