Testing the link when the index is semiparametric-a comparative study
Computational Statistics & Data Analysis
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Distribution-based similarity measures for multi-dimensional point set retrieval applications
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Consistent Nonparametric Tests of Independence
The Journal of Machine Learning Research
Nonparametric statistical inference for ergodic processes
IEEE Transactions on Information Theory
Neural Networks
The Journal of Machine Learning Research
Searching for a common pooling pattern among several samples
Computational Statistics & Data Analysis
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We present two simple and explicit procedures for testing homogeneity of two independent multivariate samples of size n. The nonparametric tests are based on the statistic Tn, which is the L1 distance between the two empirical distributions restricted to a finite partition. Both tests reject the hypothesis of homogeneity if Tn becomes large, i.e., if Tn exceeds a threshold. We first discuss Chernoff-type large deviation properties of Tn. This results in a distribution-free strong consistent test of homogeneity. Then the asymptotic distribution of the test statistic is obtained, leading to an asymptotically α-level test procedure.