A new maximum mean discrepancy based two-sample test for equal distributions in separable metric spaces

This paper presents a novel two-sample test for equal distributions in separable metric spaces, utilizing the maximum mean discrepancy (MMD). The test statistic is derived from the decomposition of the total variation of data in the reproducing kernel Hilbert space, and can be regarded as a V-statistic-based estimator of the squared MMD. The paper establishes the asymptotic null and alternative distributions of the test statistic. To approximate the null distribution accurately, a three-cumulant matched chi-squared approximation method is employed. The parameters for this approximation are consistently estimated from the data. Additionally, the paper introduces a new data-adaptive method based on the median absolute deviation to select the kernel width of the Gaussian kernel, and a new permutation test combining two different Gaussian kernel width selection methods, which improve the adaptability of the test to different data sets. Fast implementation of the test using matrix calculation is discussed. Extensive simulation studies and three real data examples are presented to demonstrate the good performance of the proposed test.