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              12月19日香港理工大學趙興球教授來我院講座預告
              ( 來源:   發布日期:2019-12-18 閱讀:次)

              講座題目:Penalized Generalized Empirical Likelihood with a Diverging Number of General Estimating Equations for Censored Data

              主講人趙興球 香港理工大學

              講座時間:2019年12月19日(周四)下午3:00—4:30

              講座地點:綜合樓601

              主講人簡介:

              趙興球,香港理工大學副教授。于2008年獲得加拿大麥克馬斯特大學(McMaster University)的統計博士學位。研究領域包括生存分析,面板計數數據,縱向數據分析,復發事件數據,高維生存模型分析,半參數和非參數方法,大偏差和中偏差理論在生存模型中的應用。她在《The Annals of Statistics》,《Journal of the American Statistical Association》等統計領域的頂級期刊上發表了許多研究論文;獲得了多項香港研究資助局基金和國家自然科學基金資助,2014年獲得教育部高等學?茖W研究優秀成果獎自然科學二等獎。她與合作者針對區間刪失數據所構建的廣義對數秩檢驗被享有盛譽的SAS軟件系統于2010年收錄;擔任副編輯的雜志包括:Journal of Applied Statistics (2017年至今),Communications in Statistics (2011年至今)等,還擔任許多統計期刊包括統計4大權威期刊的審稿人。

              講座摘要:

              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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