题目：Concordance Measure-based Feature Screening and Variable Selection
主讲：林华珍
时间：2017年11月27号 10:30-11:30
地点：明德主楼 1030会议室
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.
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。先后有论文发表在JASA、Annals of Statistics、JRSSB、Biometrika及Biometrcs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 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：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。