A multivariate nonparametric test of independence
A new nonparametric approach to the problem of testing the joint independence of two or more random vectors in arbitrary dimension is developed based on a measure of association determined by interpoint distances. The population independence coefficient takes values between 0 and 1, and equals zero if and only if the vectors are independent. We show that the corresponding statistic has a finite limit distribution if and only if the two random vectors are independent; thus we have a consistent test for independence. The coefficient is an increasing function of the absolute value of product moment correlation in the bivariate normal case, and coincides with the absolute value of correlation in the Bernoulli case. A simple modification of the statistic is affine invariant. The independence coefficient and the proposed statistic both have a natural extension to testing the independence of several random vectors. Empirical performance of the test is illustrated via a comparative Monte Carlo study.
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Volume (Year): 97 (2006)
Issue (Month): 8 (September)
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- Sinha, Bimal Kumar & Wieand, H. S., 1977. "Multivariate nonparametric tests for independence," Journal of Multivariate Analysis, Elsevier, vol. 7(4), pages 572-583, December.
- Taskinen, Sara & Kankainen, Annaliisa & Oja, Hannu, 2003. "Sign test of independence between two random vectors," Statistics & Probability Letters, Elsevier, vol. 62(1), pages 9-21, March.
- Deheuvels, Paul, 1981. "An asymptotic decomposition for multivariate distribution-free tests of independence," Journal of Multivariate Analysis, Elsevier, vol. 11(1), pages 102-113, March.
- Csörgo, Sándor, 1985. "Testing for independence by the empirical characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 290-299, June.
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