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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. Two kinds of tests are provided. Distribution-free strong consistent tests are derived on the
basis of large deviation bounds on the test statistcs: these tests make almost surely no Type I or Type II error after
a random sample size. Asymptotically alpha-level tests are obtained from the limiting distribution of the test statistics.
For the latter tests, the Type I error converges to a fixed non-zero value alpha, and the Type II error drops to zero, for
increasing sample size. All tests reject the null hypothesis of independence if the test statistics become large. The
performance of the tests is evaluated experimentally on benchmark data.