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12月25日西南财经大学林华珍教授来我院讲学预告
( 来源:   发布日期:2017-12-25 阅读:次)

讲座题目:Concordance Measure-based Feature Screening and Variable Selection

主讲人:西南财经大学 林华珍教授

讲座时间:20171225日(星期一)16:00-16:50

讲座地点:综合楼601会议室

主讲人简介:林华珍,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,入选国家百千万人才工程,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。 论文发表在JASA、Annals of Statistics、JRSSB、Biometrika、Journal of Econometrics及Biometrcs等国际统计学及计量经济学顶级期刊上,并先后担任国际统计学期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》Associate 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、函数型数据分析、时空数据分析。

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.


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