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(11月4日)(时间更改)Concordance Measure-based Feature Screening and Variable Selection
来源: 陈黎   发布时间:2017-11-1   阅读次数:1209

报告题目: Concordance Measure-based Feature Screening and Variable Selection

 

报告人: 林华珍教授

西南财经大学统计学院

时间地点:2017114(星期六)下午3:00-4:00

              浙江大学玉泉校区工商管理楼200-9报告厅 

摘要: The $C$-statistic, measuring the rank concordance between predictors and outcomes, has become a standard metric of predictive accuracy and is therefore a natural criterion for  variable screening and selection. However, as the $C$-statistic is a step function, its optimization requires brute-force search, prohibiting its direct usage in the presence of high-dimensional predictors. We develop a smoothed version of the $C$-statistic to facilitate variable screening and selection. Specifically, we propose a smoothed $C$-statistic sure screening (C-SS) method for screening ultrahigh-dimensional data, and a penalized $C$-statistic (PSC) variable selection method for regularized modeling based on the screening results. We have shown that these two coherent procedures form an integrated framework for screening and variable selection: the C-SS possesses the sure screening property, and the PSC possesses the oracle property. Specifically, the PSC achieves the oracle property if $m_n = o(n^{1/4})$, where $m_n$ is the cardinality of the set of predictors captured by the C-SS. Our extensive simulations reveal that, compared to existing procedures, our proposal is more robust and efficient. Our procedure has been applied to analyze a multiple myeloma study, and has identified several novel genes that can predict patients response to treatment.

*Joint work with Yunbei MA, Yi Li and Yi Li.

 

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联系人: 张立新教授 stazlx@zju.edu.cn

 

林华珍教授简介:西南财经大学统计学院教授、博导,统计研究中心主任,美国华盛顿大学生物统计系博士后,四川大学博士。教育部长江学者特聘教授,国家杰出青年科学基金获得者,教育部新世纪优秀人才,第十一批四川省学术和技术带头人。先后有论文发表在JASAAnnals of StatisticsJRSSBBiometrikaBiometrcs等国际统计学顶级期刊上,并担任国际统计学权威期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its InterfaceAssociate Editor 国内核心学术期刊《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委。研究领域:非参数理论和方法、转换模型、生存数据分析、函数数据分析、时空数据分析。

 

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