报告题目：Feature selection for high dimensional varying coefficient models via ordinary least squares projection
报 告 人：蒋学军 副教授
所在单位：南方科技大学
报告时间：2022年8月25日 星期四 9:30-10:30
报告地点：腾讯会议：635-946-319
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：Feature selection is a changing issue for varying coefficient models when the dimensionality of covariates is ultrahigh. The traditional technology of significantly reducing dimensionality is the marginal correlation screening method based on nonparametric smoothing. However, marginal correlation screening methods may be screen out variables that are jointly correlated to the response. To address this, we propose a novel screener with the name of group screening via nonparametric smoothing high dimensional ordinary least squares projection, referred to as “Group HOLP'” and study its sure screening property. Based on this nice property, we introduce a refined feature selection procedure via employing the extended Bayesian information criteria (EBIC) to select the suitable submodels in varying coefficient models, which is coined as Group HOLP-EBIC method. Under some regularity conditions, we establish the strong consistency of feature selection for the proposed method. The performance of our method is evaluated by simulations and further illustrated by two real examples.
报告人简介：蒋学军，现任南方科技大学统计与数据科学系长聘副教授、博士生导师，统计与数据科学系党总支书记。2009年于香港中文大学获得博士学位，09-10（2010/09-2010/09）在港中文从事博士后研究，2013年07月加入南方科技大学，入选深圳市海外高层次人才孔雀计划（2016），曾获南方科技大学杰出教学奖（2018），深圳市优秀教师（2018），主持有国家自然科学基金、广东省自然科学基金、深圳市基础研究面上项目、深圳市技术委托开发项目、广东省教学改革项目等近10项。
主要研究方向包括金融统计与计量、分位数回归、变量选择、假设检验、高维统计推断等，在数理统计和金融与计量经济统计方向做出了比较深入的研究，已在统计学主流期刊和相关金融、经济等交叉学科期刊上发表SCI&SSCI论文近50篇，授权专利1项目及在科学出版英文教材一部。
