陈夏 | Penalized empirical likelihood for high-dimensional generalized linear models

时间:2019年11月6日(周三)下午 16:00-17:00

地点:中北校区理科大楼A1716

题目:Penalized empirical likelihood for high-dimensional generalized linear models

报告人:陈夏  陕西师范大学 教授

主持人:张日权 教授

摘要:

In this paper, we develop penalized empirical likelihood for parameter estimation and variable selection in high-dimensional generalized linear models. By using adaptive lasso penalty function, we show that the proposed estimator has the oracle property. Also, we consider the problem of testing hypothesis, and show that the nonparametric profiled empirical likelihood ratio statistic has asymptotic chi-square distribution. Some simulations and an application are given to illustrate the performance of 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。


发布者:张瑛发布时间:2019-11-22浏览次数:84