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20162026
most citedJackknife empirical likelihood confidence intervals for the categorical Gini correlation

1 citations · 2 across the 6 of their papers we have counts for

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8 papers · 1 filter

stat.ME2026

Comparing Two Categorical Gini Correlations with Applications to Classification Problems

Sameera Hewage, Yongli Sang

This article proposes an inferential framework for comparing predictor importance in classification problems with categorical response variables. The approach is based on the categ…

stat.ME2023★ 1 cited

Jackknife empirical likelihood confidence intervals for the categorical Gini correlation

Sameera Hewage, Yongli Sang

The categorical Gini correlation, , was proposed by Dang et al. to measure the dependence between a categorical variable, , and a numerical variable, . It has been show…

stat.ME2023★ 1 cited

Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation

Yongli Sang, Xin Dang

Gini distance correlation (GDC) was recently proposed to measure the dependence between a categorical variable, Y, and a numerical random vector, X. It mutually characterizes indep…

stat.ME2019

Empirical Likelihood Test for Diagonal Symmetry

Yongli Sang, Xin Dang

Energy distance is a statistical distance between the distributions of random variables, which characterizes the equality of the distributions. Utilizing the energy distance, we de…

stat.ME2019

Jackknife Empirical Likelihood Approach for K-sample Tests

Yongli Sang, Xin Dang, Yichuan Zhao

The categorical Gini correlation is an alternative measure of dependence between a categorical and numerical variables, which characterizes the independence of the variables. A non…

stat.ME2019

Depth-based Weighted Jackknife Empirical Likelihood for Non-smooth U-structure Equations

Yongli Sang, Xin Dang, Yichuan Zhao

In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with -statistic structure. Jackknife empirical likelihood (JEL) approa…