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Generalized Kernel Two-Sample Tests

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If you have a question about this talk, please contact Dr Sergio Bacallado.

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.

This talk is part of the Statistics series.

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