报告人：林华珍教授 西南财经大学

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

报告摘要：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.

报告人简介：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。国家杰出青年科学基金获得者，第十一批四川省学术和技术带头人。 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。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。

报告时间：2017年10月31日(星期二)上午10：10-11：10

报告地点: 科技楼南楼702室