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Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Abstract:

We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, maximum mean discrepancies (MMD), that is, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. In the case where the energy distance is computed with a semimetric of negative type, a positive definite k...

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Publisher copy:
10.1214/13-AOS1140

Authors


Journal:
Annals of Statistics
Volume:
41
Issue:
5
Pages:
2263-2291
Publication date:
2013-10-01
DOI:
ISSN:
0090-5364
Source identifiers:
487751
Language:
English
Keywords:
Pubs id:
pubs:487751
UUID:
uuid:6805ae51-f160-48cb-9d37-43ec1b75669a
Local pid:
pubs:487751
Deposit date:
2014-11-11

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