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SUMMARY:Generalized Kernel Two-Sample Tests - Hao Chen (University of Cali
 fornia\, Davis)
DTSTART:20201127T160000Z
DTEND:20201127T170000Z
UID:TALK152560@talks.cam.ac.uk
CONTACT:Dr Sergio Bacallado
DESCRIPTION:Kernel two-sample tests have been widely used for multivariate
  data in testing equal distribution.  However\, existing tests based on ma
 pping distributions into a reproducing kernel Hilbert space do not work we
 ll 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 statis
 tic that makes use of an informative pattern under moderate and high dimen
 sions and achieves substantial power improvements over existing kernel two
 -sample tests for a wide range of alternatives.  We also propose alternati
 ve testing procedures that maintain high power with low computational cost
 \, offering easy off-the-shelf tools for large datasets.
LOCATION: https://maths-cam-ac-uk.zoom.us/j/92821218455?pwd=aHFOZWw5bzVReU
 NYR2d5OWc1Tk15Zz09
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