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Jackknife empirical likelihood confidence intervals for the Gini distance correlation

Abstract Number:

3807 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Sameera Hewage (1), Yongli Sang (1)

Institutions:

(1) University of Louisiana at Lafayette, N/A

Co-Author:

Yongli Sang  
University of Louisiana at Lafayette

First Author:

Sameera Hewage  
University of Louisiana at Lafayette

Presenting Author:

Sameera Hewage  
University of Louisiana at Lafayette

Abstract Text:

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.

Keywords:

Gini distance correlation|Confidence interval|Jackknife empirical likelihood| | |

Sponsors:

Section on Nonparametric Statistics

Tracks:

Applications of nonparametric methods

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