Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing

TL;DR

This paper introduces a new class of strictly proper kernel scoring rules and divergences, enhancing two-sample hypothesis testing by integrating multiple divergence measures for improved accuracy.

Contribution

It proposes a general Kernel Scoring rule and Kernel Divergence, connecting them to existing measures like MMD, and demonstrates their effectiveness in hypothesis testing.

Findings

Kernel Score includes MMD as a special case

Kernel Score captures more distribution information than MMD

Combining multiple Kernel Divergences improves testing accuracy

Abstract

We study strictly proper scoring rules in the Reproducing Kernel Hilbert Space. We propose a general Kernel Scoring rule and associated Kernel Divergence. We consider conditions under which the Kernel Score is strictly proper. We then demonstrate that the Kernel Score includes the Maximum Mean Discrepancy as a special case. We also consider the connections between the Kernel Score and the minimum risk of a proper loss function. We show that the Kernel Score incorporates more information pertaining to the projected embedded distributions compared to the Maximum Mean Discrepancy. Finally, we show how to integrate the information provided from different Kernel Divergences, such as the proposed Bhattacharyya Kernel Divergence, using a one-class classifier for improved two-sample hypothesis testing results.