B-test: A Non-parametric, Low Variance Kernel Two-sample Test

101 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “data, Zhang et al. (2008) proposes a Structured-HSIC where P satisfies the conditional independence specified by an undirected graphical model, e.”
Detecting and Correcting for Label Shift with Black Box Predictors (2018, methodology): “MMD B-test on Oracle KS-test (c) Power at α = 0 .”
Interpretable Distribution Features with Maximum Testing Power (2016, background): “GMD with 100 dimensions turns out to be an easy problem for all the tests except MMD-lin.”
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing (2015, methodology): “O ( n 2 ) to compute, Gretton et al. [2012a] and Zaremba et al. [2013] introduce linear-time and block-based subquadratic-time statistics gMMD 2 l and gMMD 2 b .”
Fast Two-Sample Testing with Analytic Representations of Probability Measures (2015, methodology): “We consider a two-sample test on certain extremely low-level features in the dataset - kinematic properties measured by the particle detectors, i.e., the joint distributions of the azimuthal angular momenta ϕ for four particle jets.”
Large-scale kernel methods for independence testing (2016, background): “Regarding the choice of B, Zaremba et al. (2013) discussed that the null distribution is close to that guaranteed by the CLT when B is small, and hence, the Type I error will be closer to the desired level.”