On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests

• Main Authors: Aaditya Ramdas, Nicolás García Trillos, Marco Cuturi
• Format: Article
• Language: English
• Published: MDPI AG 2017-01-01
• Series: Entropy
• Subjects: two-sample testing; wasserstein distance; entropic smoothing; energy distance; maximum mean discrepancy; QQ and PP plots; ROC and ODC curves
• Online Access: http://www.mdpi.com/1099-4300/19/2/47

Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and rich, with a wide variety of statistics having being designed and analyzed, both for the unidimensional and the multivariate setting. Inthisshortsurvey,wefocusonteststatisticsthatinvolvetheWassersteindistance. Usingan entropic smoothing of the Wasserstein distance, we connect these to very different tests including multivariate methods involving energy statistics and kernel based maximum mean discrepancy and univariate methods like the Kolmogorov–Smirnov test, probability or quantile (PP/QQ) plots and receiver operating characteristic or ordinal dominance (ROC/ODC) curves. Some observations are implicit in the literature, while others seem to have not been noticed thus far. Given nonparametric two-sample testing’s classical and continued importance, we aim to provide useful connections for theorists and practitioners familiar with one subset of methods but not others.