Jackknife Empirical Likelihood Approach for K-sample Tests

2019/08/01 by Yongli Sang, Sang, Yongli, Xin Dang +3
Economics, Econometrics and Finance · Mathematics · #62G20 #62G35 #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1908.00477

openalex publication_date 2019/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The categorical Gini correlation is an alternative measure of dependence between a categorical and numerical variables, which characterizes the independence of the variables. A nonparametric test for the equality of K distributions has been developed based on the categorical Gini correlation. By applying the jackknife empirical likelihood approach, the standard limiting chi-square distribution with degree freedom of K-1 is established and is used to determine critical value and p-value of the test. Simulation studies show that the proposed method is competitive to existing methods in terms of power of the tests in most cases. The proposed method is illustrated in an application on a real data set.

Citations

• Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
• Adjusted Empirical Likelihood and its Properties
• A test for the two-sample problem based on empirical characteristic functions
• A new Gini correlation between quantitative and qualitative variables

Related

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• JEL ratio test for independence between a continuous and a categorical random variable
• Asymptotic Normality of Gini Correlation in High Dimension with Applications to the K-sample Problem
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