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.
- Speaker: Hao Chen (University of California, Davis)
- Friday 27 November 2020, 16:00–17:00
- Venue: https://maths-cam-ac-uk.zoom.us/j/92821218455?pwd=aHFOZWw5bzVReUNYR2d5OWc1Tk15Zz09.
- Series: Statistics; organiser: Dr Sergio Bacallado.