A multivariate nonparametric test of independence

• Published in: Journal of multivariate analysis 2006-09, Vol.97 (8), p.1742-1756
• Main Authors: Bakirov, Nail K., Rizzo, Maria L., Székely, Gábor J.
• Format: Article
• Language: English
• Subjects: Algebra; Empirical characteristic function; Independence; Independence coefficient; Monte Carlo simulation; Multivariate analysis; Studies; Testing independence; Testing independence Independence coefficient Empirical characteristic function
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2005.10.005

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.