A nonparametric two-sample test using a parametric integral probability metric
Detecting distributional differences between two independent samples is a fundamental problem in statistics and machine learning. Nonparametric two-sample testing provides a principled framework for determining whether two samples are drawn from the same underlying distribution, without assuming any specific parametric form for the distribution. In this study, we propose a new two-sample test statistic based on a newly introduced integral probability metric (IPM), using a specially designed parametric discriminator class with a single node of a neural network. We show that the resulting test statistic, called PReLU-IPM, is nonparametric and establish theoretical guarantees for the associa...
| Authors | Yuha Park, Yongdai Kim |
|---|---|
| Primary category | stat.ML |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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