12.24 题目:Penalized Empirical Likelihood for the Sparse Cox Model

发布时间:2019-12-23   浏览次数:61


时间:12月24日下午3:00-4:30

地点:博识楼434

讲座人:赵亦川(美国佐治亚州立大学数学与统计系终身教授)

讲座题目:Penalized Empirical Likelihood for the Sparse Cox Model

内容简介:The current penalized regression methods for selecting predictor variables and estimating the associated regression coefficients in the Cox model are mainly based on partial likelihood. In this paper, an empirical likelihood method is proposed for the Cox model in conjunction with appropriate penalty functions when the dimensionality of data is high. Theoretical properties of the resulting estimator for the large sample are proved. Simulation studies suggest that empirical likelihood works better than partial likelihood in terms of selecting correct predictors without introducing more model errors. The well-known primary biliary cirrhosis data set is used to illustrate the proposed empirical likelihood method.

 

讲座人简介:赵亦川,美国佐治亚州立大学数学与统计系终身教授,佐治亚州立大学神经科学研究所和公共卫生学院教授,研究领域包括Survival Analysis, Empirical Likelihood Method, Non-parametric Statistics, Analysis of ROC Curve, Bioinformatics, Monte Carlo Methods and Statistical Modeling of Fuzzy Systems等。赵亦川教授在广泛的统计研究领域发表研究论文90余篇,是三本统计学、生物统计和数据科学专著的编委,他目前在担任多家统计杂志担任主编或编委会成员,是International Statistics Institute的当选会员,泛华统计协会理事会委员。