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Conference Paper

Nonparametric Independence Tests: Space Partitioning and Kernel Approaches

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gretton, A. (2008). Nonparametric Independence Tests: Space Partitioning and Kernel Approaches. Algorithmic Learning Theory: 19th International Conference (ALT08), 183-198.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C6C7-A
Abstract
Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. All tests reject the null hypothesis of independence if the test statistics become large. The large deviation and limit distribution properties of all three test statistics are given. Following from these results, distributionfree strong consistent tests of independence are derived, as are asymptotically alpha-level tests. The performance of the tests is evaluated experimentally on benchmark data.