Hoeffding's independence test

In statistics, Hoeffding's test of independence, named after Wassily Hoeffding, is a test based on the population measure of deviation from independence

${\displaystyle H=\int (F_{12}-F_{1}F_{2})^{2}\,dF_{12}}$

where ${\displaystyle F_{12}}$ is the joint distribution function of two random variables, and ${\displaystyle F_{1}}$ and ${\displaystyle F_{2}}$ are their marginal distribution functions. Hoeffding derived an unbiased estimator of ${\displaystyle H}$ that can be used to test for independence, and is consistent for any continuous alternative. The test should only be applied to data drawn from a continuous distribution, since ${\displaystyle H}$ has a defect for discontinuous ${\displaystyle F_{12}}$, namely that it is not necessarily zero when ${\displaystyle F_{12}=F_{1}F_{2}}$. This drawback can be overcome by taking an integration with respect to ${\displaystyle dF_{1}F_{2}}$. This modified measure is known as Blum–Kiefer–Rosenblatt coefficient.^[1]

A paper published in 2008^[2] describes both the calculation of a sample based version of this measure for use as a test statistic, and calculation of the null distribution of this test statistic.

• Correlation
• Kendall's tau
• Spearman's rank correlation coefficient
• Distance correlation

• Wassily Hoeffding, A non-parametric test of independence, Annals of Mathematical Statistics 19: 293–325, 1948. (JSTOR)
• Hollander and Wolfe, Non-parametric statistical methods (Section 8.7), 1999. Wiley.

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