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Ball divergence is a non-parametric two-sample statistical test method in metric spaces. It measures the difference between two population probability distributions by integrating the difference over all balls in the space[1]. Therefore, its value is zero if and only if the two probability measures are the same. Similar to common non-parametric test methods, ball divergence calculates the p-value through permutation tests.
Background
editDistinguishing between two unknown samples in multivariate data is an important and challenging task. Previously, a more common non-parametric two-sample test method was the energy distance test[2]. However, the effectiveness of the energy distance test relies on the assumption of moment conditions, making it less effective for extremely imbalanced data (where one sample size is disproportionately larger than the other). To address this issue, Chen, Dou, and Qiao proposed a non-parametric multivariate test method using ensemble subsampling nearest neighbors (ESS-NN) for imbalanced data[3]. This method effectively handles imbalanced data and increases the test's power by fixing the size of the smaller group while increasing the size of the larger group.
Additionally, Gretton et al. introduced the maximum mean discrepancy (MMD) for the two-sample problem[4]. Both methods require additional parameter settings, such as the number of groups 𝑘 in ESS-NN and the kernel function in MMD. Ball divergence addresses the two-sample test problem for extremely imbalanced samples without introducing other parameters.
Definition
editLet's start with the population ball divergence. Suppose that we have a metric space ( ), where norm introduces a metric for two point in space by . Besides, we use to show a closed ball with the center and radius . Then, the population ball divergence of Borel probability measures is
For convenience, we can decompose the Ball Divergence into two parts:
and
Thus
Next, we will introduce the sample ball divergence. Let denote whether point locates in the ball . Given two independent samples form and form
where means the proportion of samples from the probability measure located in the ball and means the proportion of samples from the probability measure located in the ball . Meanwhile, and means the proportion of samples from the probability measure and located in the ball . The sample versions of and are as follows
Finally, we can give the sample ball divergence
Properties
edit1. Given two Borel probability measures and on a finite dimensional Banach space , then where the equality holds if and only if .
2. Suppose and are two Borel probability measures in a separable Banach space . Denote their support and , if or , then we have where the equality holds if and only if .
3.Consistency: We have
where for some .
Define , and then let where
The function has spectral decomposition: where and are the eigenvalues and eigenfunctions of . For , are i.i.d. , and
4.Asymptotic distribution under the null hypothesis: Suppose that both and in such a way that . Under the null hypothesis, we have
5. Distribution under the alternative hypothesis: let Suppose that both and in such a way that . Under the alternative hypothesis, we have
6. The test based on is consistent against any general alternative . More specifically, and More importantly, can also be expressed as which is independent of .
References
edit- ^ Pan, Wenliang; Tian, Yuan; Wang, Xueqin; Zhang, Heping (2018-06-01). "Ball Divergence: Nonparametric two sample test". The Annals of Statistics. 46 (3): 1109–1137. doi:10.1214/17-AOS1579. ISSN 0090-5364. PMC 6192286. PMID 30344356.
- ^ Székely, Gábor J.; Rizzo, Maria L. (August 2013). "Energy statistics: A class of statistics based on distances". Journal of Statistical Planning and Inference. 143 (8): 1249–1272. doi:10.1016/j.jspi.2013.03.018. ISSN 0378-3758.
- ^ Chen, Lisha; Dou, Winston Wei; Qiao, Zhihua (December 2013). "Ensemble Subsampling for Imbalanced Multivariate Two-Sample Tests". Journal of the American Statistical Association. 108 (504): 1308–1323. doi:10.1080/01621459.2013.800763. ISSN 0162-1459.
- ^ Gretton, Arthur; Borgwardt, Karsten M.; Rasch, Malte; Schölkopf, Bernhard; Smola, Alexander J. (2007-09-07), "A Kernel Method for the Two-Sample-Problem", Advances in Neural Information Processing Systems 19, The MIT Press, pp. 513–520, doi:10.7551/mitpress/7503.003.0069, hdl:1885/37327, ISBN 978-0-262-25691-9, retrieved 2024-06-28