A Martingale Kernel Two-Sample Test

Fuente: arXiv
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Main Authors: Chatterjee, Anirban, Ramdas, Aaditya
Format: Preprint
Published: 2025
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author Chatterjee, Anirban
Ramdas, Aaditya
author_facet Chatterjee, Anirban
Ramdas, Aaditya
contents The Maximum Mean Discrepancy (MMD) is a widely used multivariate distance metric for two-sample testing. The standard MMD test statistic has an intractable null distribution typically requiring costly resampling or permutation approaches for calibration. In this work we leverage a martingale interpretation of the estimated squared MMD to propose martingale MMD (mMMD), a quadratic-time statistic which has a limiting standard Gaussian distribution under the null. Moreover we show that the test is consistent against any fixed alternative and for large sample sizes, mMMD offers substantial computational savings over the standard MMD test, with only a minor loss in power.
format Preprint
id arxiv_https___arxiv_org_abs_2510_11853
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Martingale Kernel Two-Sample Test
Chatterjee, Anirban
Ramdas, Aaditya
Methodology
Statistics Theory
The Maximum Mean Discrepancy (MMD) is a widely used multivariate distance metric for two-sample testing. The standard MMD test statistic has an intractable null distribution typically requiring costly resampling or permutation approaches for calibration. In this work we leverage a martingale interpretation of the estimated squared MMD to propose martingale MMD (mMMD), a quadratic-time statistic which has a limiting standard Gaussian distribution under the null. Moreover we show that the test is consistent against any fixed alternative and for large sample sizes, mMMD offers substantial computational savings over the standard MMD test, with only a minor loss in power.
title A Martingale Kernel Two-Sample Test
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2510.11853