In order to adapt the Wasserstein distance to the large sample multivariate non-parametric two-sample problem, making its application computationally feasible, permutation tests based on the Sinkhorn divergence between probability vectors associated to data dependent partitions are considered. Different ways of implementing these tests are evaluated and the asymptotic distribution of the underlying statistic is established in some cases. The statistics proposed are compared, in simulated examples, with the test of Schilling's, one of the best non-parametric tests available in the literature.

翻译：为了使瓦森斯坦距离适应大型样本多变量非参数两样抽样问题,使其应用在计算上是可行的,将考虑根据与数据依赖区隔相关的辛克霍恩矢量之间概率差异的差异进行变换试验,评估了实施这些试验的不同方式,并在某些情况下确定了基本统计数据的无症状分布,在模拟实例中,将拟议的统计数据与Schilling的测试进行比较,这是文献中现有的最佳非参数测试之一。