报告题目：Empirical likelihood in semiparametric models

报告时间：2020-12-11下午3：00--4：00

报告地点：数学楼2-2

报告摘要:

In this talk, we discuss the empirical likelihood based inference problem in semiparametric models. Firstly, we investigate the empirical likelihood based inference for the parameters in a partially linear single-index model. We propose a bias correction method to achieve that the empirical likelihood ratio has standard chi-square limit. Secondly, we investigate the empirical likelihood-based inference for a varying coefficient model with longitudinal data. we propose three empirical likelihood ratios: the naive empirical likelihood ratio, the mean-corrected empirical likelihood ratio and the residual-adjusted empirical likelihood ratio, and show that these ratios have chi-square limits. In addition, when some components are of particular interest, we suggest the mean-corrected and residual-adjusted partial empirical likelihood ratios for the construction of the confidence regions/intervals. A simulation study is undertaken to compare the empirical likelihood and the normal approximation methods in terms of coverage accuracies and average areas/widths of confidence regions/intervals. An example in epidemiology is used for illustration.

个人简介：

薛留根，北京工业大学教授，博士生导师。主要学术兼职：中国现场统计研究会理事及生存分析分会副理事长等。研究方向：非参数统计与数据分析；主要研究兴趣包括：非参数与半参数模型的统计推断、复杂数据统计分析与建模、经验似然等。主持国家和省部级科研项目15项，其中连续5次获国家自然科学基金资助。出版著作8部，其中3部专著。在《Journal of the American Statistical Association》、《Journal of the Royal Statistical Society，Series B》、《The Annals of Statistics》、《Biometrika》等学术期刊上发表论文260余篇，其中3篇为高被引论文。以第一完成人获教育部自然科学二等奖1项，获全国统计科学研究优秀成果一等奖1项。已招收研究生65人，其中博士研究生20人，硕士研究生45人；在指导的研究生中，1人获北京市优秀博士学位论文以及全国优秀博士学位论文提名奖，1人获全国统计科学研究优秀成果博士学位论文二等奖。