Generalized Kernel Two-Sample Tests

With Hao Chen (University of California, Davis)

Generalized Kernel Two-Sample Tests

Kernel two-sample tests have been widely used for multivariate data in testing equal distribution. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space do not work well for some common alternatives when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of an informative pattern under moderate and high dimensions and achieves substantial power improvements over existing kernel two-sample tests for a wide range of alternatives. We also propose alternative testing procedures that maintain high power with low computational cost, offering easy off-the-shelf tools for large datasets.

Add to your calendar or Include in your list

How can mathematics help us to understand the behaviour of ants? Read more about the fanscinating work being carri… https://t.co/iCODvvxqE6 View on Twitter