10. Jackknife empirical likelihood for the categorical Gini correlation

The categorical Gini correlation, ρg, is a recently proposed measure of dependence between categorical variable, Y, and a numerical random vector, X. It has been shown that ρg has more appealing properties than current existing dependence measurements. In this study, we develop the jackknife empirical likelihood (JEL) method for ρg. Confidence intervals for the Gini correlation are constructed without estimating the asymptotic variance. Adjusted and weighted JEL are explored to improve the performance of the standard JEL. Simulation studies show that our methods are competitive to existing methods in terms of coverage accuracy and shortness of confidence intervals. The proposed methods are illustrated in an application on two real datasets.

Categorical Gini correlation

Jackknife empirical likelihood

Wilk’s theorem