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Conference

Hypothesis testing using pairwise distances and associated kernels

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, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS d...

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Volume:
2
Pages:
1111-1118
Publication date:
2012-01-01
URN:
uuid:86b137e6-e693-4f20-9b91-9624382593e7
Source identifiers:
487756
Local pid:
pubs:487756
ISBN:
9781450312851

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