Jackknife empirical likelihood confidence intervals for the Gini distance correlation

Oregon Convention Center 

The Gini distance correlation (GDC) is a recently proposed dependence measure to assess the relationship between a categorical variable and a numerical variable. GDC has been found to possess more attractive properties than existing measures of dependence. In this study, we develop the jackknife empirical likelihood (JEL) approach for the GDC. We then build confidence intervals for the correlation without estimating the asymptotic variance. In addition, we explore adjusted and weighted JEL methods to enhance the standard JEL's performance. Simulation studies demonstrate that our approaches are competitive with existing methods in terms of coverage accuracy and the shortness of confidence intervals. The proposed methods are illustrated using a real-data example.

Gini distance correlation

Confidence interval

Jackknife empirical likelihood 

Section on Nonparametric Statistics