Penalized empirical likelihood for high-dimensionalgeneralized linear models with longitudinal data
报告人:陈夏教授,陕西师范大学 时间:2019年11月8号,10:30-11:30
报告地点:行健楼526
 
邀请人:高启兵教授
摘要:In this talk, we consider the application of penalized empirical likelihood to the
high-dimensional generalized linear models with longitudinal data. Under regular conditions,it is shown that the penalized empirical likelihood has the oracle property. That is, thepenalized empirical likelihood estimators correctly select covariates with nonzero coefficientswith probability converging to one and that the estimators of nonzero coefficients have thesame asymptotic distribution that they would have if zero coefficients were known in advance.Also, we find the asymptotic distribution of the penalized empirical likelihood ratio teststatistic is the chi-square distribution. Thus the confidence regions can be constructed.Some simulations and a real data analysis are conducted to illustrate the proposed method.
 
个人简介:陈夏,陕西师范大学数学与信息科学学院副院长,教授。武汉大学概率论与数理统计专业博士,北京师范大学博士后,陕西省统计学学会副理事长,中国现场统计研究会大数据统计分会理事。主要研究方向是高维数据统计分析,目前已发表论文20余篇,主持国家自然科学基金2项,教育部人文社科研究项目和陕西省自然科学基础研究计划项目各1项。