报告题目： Threshold selection in feature screening for error rate control
报告人： 郭旭 副教授（北京师范大学）
报告时间： 2021年12月16日（星期四）14:00-15:00
报告地点： 腾讯会议（ID：920 588 599）
校内联系人：牛一 副教授 联系电话：84708351-8081
报告摘要: Hard thresholding rule is commonly adopted in feature screening procedures to screen out unimportant predictors for ultrahigh-dimensional data. However, different thresholds are required to adapt to different contexts of screening problems and an appropriate thresholding magnitude usually varies from the model and error distribution. With an ad-hoc choice, it is unclear whether all of the important predictors are selected or not, and it is very likely that the procedures would include many unimportant features. We introduce a data-adaptive threshold selection procedure with error rate control, which is applicable to most kinds of popular screening methods. The key idea is to apply the sample-splitting strategy to construct a series of statistics with marginal symmetry property and then to utilize the symmetry for obtaining an approximation to the number of false discoveries. We show that the proposed method is able to asymptotically control the false discovery rate and per family error rate under certain conditions and still retains all of the important predictors. Three important examples are presented to illustrate the merits of the new proposed procedures. Numerical experiments indicate that the proposed methodology works well for many existing screening methods.
报告人简介：郭旭博士，现为北京师范大学统计学院副教授，博士生导师。他于2014年获得香港浸会大学博士学位。郭旭自2018年9月至2020年2月作为助理研究教授访问美国宾州州立大学统计系。郭旭一直从事模型设定检验、高维数据分析和半参数回归分析等方面的研究，在包括统计学顶级期刊JRSSB, JASA和Biometrika等SCI和SSCI期刊发表论文40篇左右，为包括Econometrica，JASA，Journal of Econometrics，Statistica Sinica等统计学和计量经济学期刊审稿。先后主持国家自然科学基金青年基金和国家自然科学基金面上项目以及北京市自然科学基金面上项目等省部级项目。提出了模型自适应的概念和方法，解决了模型设定检验中的维数祸根问题；提出了变换充分降维的理论方法，搭建了线性充分降维和非线性充分降维的桥梁；提出了基于样本分割的FDR控制方法，实现了在无p值下的FDR控制。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”。
