Kernels Based Tests with Non-asymptotic Bootstrap Approaches for Two-sample Problems

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Interpretable Distribution Features with Maximum Testing Power (2016, methodology): “…and identically (i.i.d.) from P , and Y := { y i } ni =1 ⊂ R d from Q. Non-parametric two-sample tests based on RKHS distances (Eric et al., 2008; Fromont et al., 2012; Gretton et al., 2012a) or energy distances (Székely and Rizzo, 2004; Baringhaus and Franz, 2004) have as their test statistic…”
A Kernel Test of Goodness of Fit (2016, methodology): “The wild bootstrap technique (Shao, 2010; Leucht & Neumann, 2013; Fromont et al., 2012) addresses both problems.”
B-test: A Non-parametric, Low Variance Kernel Two-sample Test (2013, background): “These include the maximum mean discrepancy [9, 6] (of which the energy distance is an example [15, 2, 19]), which is the distance between expected features of P and Q in the RHKS; the kernel Fisher discriminant [11], which is the distance between expected feature maps normalized by the feature space”