Nonparametric tests of independence based on interpoint distances

Lingzhe Guo and Reza Modarres

Journal of Nonparametric Statistics, 2020, vol. 32, issue 1, 225-245

Abstract: We present novel tests for the hypothesis of independence when the number of variables is larger than the number of vector observations. We show that two multivariate normal vectors are independent if and only if their interpoint distance are independent. The proposed test statistics exploit different properties of the sample interpoint distances. A simulation study compares the new tests with three existing tests under various scenarios, including monotone and non-monotone dependence structures. Numerical results show that the new methods are effective for independence testing.

Date: 2020
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DOI: 10.1080/10485252.2020.1714613

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