4.5 Article

DISTANCE COVARIANCE IN METRIC SPACES

期刊

ANNALS OF PROBABILITY
卷 41, 期 5, 页码 3284-3305

出版社

INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOP803

关键词

Negative type; hypothesis testing; independence; distance correlation; Brownian covariance

资金

  1. NSF [DMS-10-07244]
  2. Microsoft Research

向作者/读者索取更多资源

We extend the theory of distance (Brownian) covariance from Euclidean spaces, where it was introduced by Szekely, Rizzo and Bakirov, to general metric spaces. We show that for testing independence, it is necessary and sufficient that the metric space be of strong negative type. In particular, we show that this holds for separable Hilbert spaces, which answers a question of Kosorok. Instead of the manipulations of Fourier transforms used in the original work, we use elementary inequalities for metric spaces and embeddings in Hilbert spaces.

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