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A Jackknife empirical likelihood approach for K ‐sample Tests
Author(s) -
Sang Yongli,
Dang Xin,
Zhao Yichuan
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11611
Subject(s) - jackknife resampling , categorical variable , empirical likelihood , mathematics , statistics , independence (probability theory) , econometrics , degrees of freedom (physics and chemistry) , resampling , correlation , chi square test , parametric statistics , confidence interval , physics , geometry , quantum mechanics , estimator
The categorical Gini correlation is an alternative measure of dependence between categorical and numerical variables, which characterizes the independence of the variables. A non‐parametric test based on the categorical Gini correlation for the equality of K distributions is developed. By applying the jackknife empirical likelihood approach, the standard limiting chi‐squared distribution with degrees of freedom of K − 1 is established and is used to determine the critical value and p ‐value of the test. Simulation studies show that the proposed method is competitive with existing methods in terms of power of the tests in most cases. The proposed method is illustrated in an application on a real dataset.