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12月19日香港理工大学赵兴球教授来我院讲座预告
( 来源:   发布日期:2019-12-18 阅读:次)

讲座题目:Penalized Generalized Empirical Likelihood with a Diverging Number of General Estimating Equations for Censored Data

主讲人赵兴球 香港理工大学

讲座时间:2019年12月19日(周四)下午3:00—4:30

讲座地点:综合楼601

主讲人简介:

赵兴球,香港理工大学副教授。于2008年获得加拿大麦克马斯特大学(McMaster University)的统计博士学位。研究领域包括生存分析,面板计数数据,纵向数据分析,复发事件数据,高维生存模型分析,半参数和非参数方法,大偏差和中偏差理论在生存模型中的应用。她在《The Annals of Statistics》,《Journal of the American Statistical Association》等统计领域的顶级期刊上发表了许多研究论文;获得了多项香港研究资助局基金和国家自然科学基金资助,2014年获得教育部高等学校科学研究优秀成果奖自然科学二等奖。她与合作者针对区间删失数据所构建的广义对数秩检验被享有盛誉的SAS软件系统于2010年收录;担任副编辑的杂志包括:Journal of Applied Statistics (2017年至今),Communications in Statistics (2011年至今)等,还担任许多统计期刊包括统计4大权威期刊的审稿人。

讲座摘要:

This talk considers simultaneous variable selection and parameter estimation as well as hypothesis testing in censored survival models without a parametric likelihood available. For the problem, we utilize certain growing dimensional general estimating equations and propose a penalized where the general estimating equations are constructed based on the semiparametric efficiency bound of estimation with given moment conditions. The proposed penalized generalized empirical likelihood estimators enjoy the oracle properties, and the estimator of any fixed dimensional vector of nonzero parameters achieves the semiparametric efficiency bound asymptotically. Furthermore, we show that the penalized generalized empirical likelihood ratio test statistic has an asymptotic central chi-square distribution. The conditions of local and restricted global optimality of weighted penalized generalized empirical likelihood estimators are also discussed. We present a two-layer iterative algorithm for efficient implementation, and investigate its convergence property. The performance of the proposed methods is demonstrated by extensive simulation studies, and a real data example is provided for illustration.

 

 

 

 



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