Using the Sinkhorn divergence in permutation tests for the multivariate two-sample problemIn 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.
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