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Activity Number: 173 - Recent Advances in Statistical Learning and Missing Data Handling
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Korean International Statistical Society
Abstract #309721
Title: A Generalized Kernel Two-Sample Test
Author(s): Hoseung Song* and Hao Chen
Companies: University of California, Davis and University of California, Davis
Keywords: kernel methods; nonparametric; high-dimensional data; general alternatives

Kernel two-sample tests have been widely used for high-dimensional data as an elegant nonparametric framework of testing equal distribution. However, existing tests using kernel embeddings of probability distributions into reproducing kernel Hilbert spaces (RKHS) lack power either for location or for scale alternatives when the dimension of the data is moderate to high due to the curse of dimensionality. The new test statistic makes use of unique patterns under high dimension and achieves substantial power improvement over existing kernel two-sample tests for general alternatives. We illustrate new approach to an analysis of New York City taxi data.

Authors who are presenting talks have a * after their name.

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