报告题目：Concordance measure-based feature screening and variable selection

报告人：林华珍 教授

报告时间：2017年12月3日 (星期日) 14:00-14:50

报告地点：尚贤楼1楼108报告厅

主持人: 张建伟院长/教授

欢迎广大师生参加！

报告内容简介：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.

专家简介：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。先后有论文发表在Annals of Statistics、JRSSB、Biometrika及Biometrcs等国际统计学顶级期刊上，为国际统计学权威期刊《Biometrics》 《Scandinavian Journal of Statistics》 《Statistics and Its Interface》 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。研究领域为非参数理论和方法、转换模型、生存数据分析、函数数据分析、时空数据分析。

数学与统计学院

江苏省统计科学研究基地

2017年11月30日