主 题：Jackknife Empirical Likelihood Interval Estimators for the Gini Index
主讲人：Prof. Yichuan Zhao
主持人:林华珍教授
时 间：2015年6月29日下午3：00-4：00
地 点：通博楼B座212会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
Yichuan Zhao has a B.S. degree and an M.S. degree in mathematics from Peking University, China and an M.S. degree in stochastics and operations research from Utrecht University, Utrecht, The Netherlands. He received his Ph.D. degree in statistics from the Department of Statistics at Florida State University, Tallahassee, Florida, in 2002 under the direction of Professor Ian McKeague. His current research interest focuses on survival analysis, empirical likelihood method, nonparametric statistics, analysis of ROC curves, bioinformatics, Monte Carlo methods, and statistical modeling of fuzzy systems. He has a joint appointment as Associate Member of the Neuroscience Institute, and he is also an Affiliated Faculty Member of PUHR at Georgia State University, Georgia, USA.
内容提要：
A variety of statistical methods have been developed to the interval estimation of a Gini index, one of the most widely used measures of economic inequality. However there are still plenty of room to improve in term of coverage accuracy, interval length, reliability and computational efficiency. In this note, we propose interval estimators for the index and the difference of two Gini indexes via jackknife empirical likelihood. Via expressing the estimating equations in form of U-statistics, our method can be simply applied as the standard empirical likelihood for a univariate mean and avoid maximizing the profile empirical likelihood for the difference of two Gini indexes. Simulation studies show that our method is comparable to existing empirical likelihood methods in term of coverage accuracy, but yield shorter intervals. The proposed method is illustrated via analyzing a previously published real data set. It is joint work with DongliangWang and Dirk W. Gilmore.
