报告题目：Jackknife Empirical Likelihood Methods for Gini Correlations and their Equality Testing

报告摘要：The Gini correlation plays an important role in measuring dependence of random variables with
heavy tailed distributions, whose properties are a mixture of Pearson's and Spearman's correlations. Due to the structure of this dependence measure, there are two Gini correlations
between each pair of random variables, which are not equal in general. Both the Gini correlation and the equality of the two Gini correlations play important roles in Economics. In the literature, there are limited papers focusing on the inference of the Gini correlations and their equality testing.

We have developed the jackknife empirical likelihood(JEL) approach for the single Gini correlation, for testing the equality of the two Gini correlations, and for the Gini correlations' differences of two independent samples. The standard limiting chi-square distributions of those
jackknife empirical likelihood ratio statistics are established and used to construct confidence intervals, rejection regions, and to calculate $p$-values of the tests.

Simulation studies show that our methods are competitive to existing methods in terms of coverage accuracy and shortness of confidence intervals, as well as in terms of power of the tests. The proposed methods are illustrated in an application on a real data set from UCI Machine Learning Repository.

报告人：桑永丽，博士，路易斯安那大学拉法叶助理教授，博士生导师。
2009年毕业于bevictor伟德数学本科，2012年于华中师范大学获得数学硕士，2017年于美国密西西比大学获得统计博士。从事统计方法和数据分析的研究，主要包括时间序
列和非参估计。研究成果先后在Journal of Statistical Planning and Inference, Journal of Time Series Analysis, Canadian Journal of Statistics等杂志发表。

报告时间：2018年6月20号（周三）上午9:00-10:00
报告地点：长清校区B434报告厅
欢迎各位老师和同学参加！