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June 2018 Ball Divergence: Nonparametric two sample test
Wenliang Pan, Yuan Tian, Xueqin Wang, Heping Zhang
Ann. Statist. 46(3): 1109-1137 (June 2018). DOI: 10.1214/17-AOS1579

Abstract

In this paper, we first introduce Ball Divergence, a novel measure of the difference between two probability measures in separable Banach spaces, and show that the Ball Divergence of two probability measures is zero if and only if these two probability measures are identical without any moment assumption. Using Ball Divergence, we present a metric rank test procedure to detect the equality of distribution measures underlying independent samples. It is therefore robust to outliers or heavy-tail data. We show that this multivariate two sample test statistic is consistent with the Ball Divergence, and it converges to a mixture of $\chi^{2}$ distributions under the null hypothesis and a normal distribution under the alternative hypothesis. Importantly, we prove its consistency against a general alternative hypothesis. Moreover, this result does not depend on the ratio of the two imbalanced sample sizes, ensuring that can be applied to imbalanced data. Numerical studies confirm that our test is superior to several existing tests in terms of Type I error and power. We conclude our paper with two applications of our method: one is for virtual screening in drug development process and the other is for genome wide expression analysis in hormone replacement therapy.

Citation

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Wenliang Pan. Yuan Tian. Xueqin Wang. Heping Zhang. "Ball Divergence: Nonparametric two sample test." Ann. Statist. 46 (3) 1109 - 1137, June 2018. https://doi.org/10.1214/17-AOS1579

Information

Received: 1 November 2015; Revised: 1 February 2017; Published: June 2018
First available in Project Euclid: 3 May 2018

zbMATH: 1395.62101
MathSciNet: MR3797998
Digital Object Identifier: 10.1214/17-AOS1579

Subjects:
Primary: 62H15
Secondary: 62G10

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.46 • No. 3 • June 2018
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