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We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
3 lut 2021 · We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from...
28 lis 2023 · The test is based on the comparison of the distributions of the distances between the samples’ elements and their means using univariate two-sample Kolmogorov-Smirnov test.
In this paper, we present a new non-parametric two-sample test statistic aiming for the high-dimensional setting based on a kernel projected Wasserstein (KPW) distance, with a nonlinear projector based on the reproducing kernel Hilbert space (RKHS) designed to optimize the test power via maximizing the probability distance between the distributi...
Two-sample Test using Projected Wasserstein Distance. Jie Wang, Rui Gao, and Yao Xie. Abstract—We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
We develop a projected Wasserstein distance for the two-sample test, a fundamental problem in statistics and machine learning: given two sets of samples, to determine whether they are from the same distribution.
In this paper, we study a class of two sample test statistics based on inter-point distances in the high dimensional and low/medium sample size setting. Our test statistics include the well-known
