报告题目：Quantile correlation-based variable selection

报告人：唐年胜教授

报告时间：2021年5月20日（周四）下午14:00-15:00

报告地点：腾讯会议 ID：591 524 989

报告人简介：唐年胜，云南大学二级教授、开云·体育(中国)官方网站院长、博士生导师，国家杰出青年科学基金获得者，教育部“长江学者”特聘教授，国家百千万人才工程暨有突出贡献中青年专家，享受国务院政府特殊津贴，国际数理统计学会会士、国际统计学会推选会员，云南省高等学校教学名师，中国现场统计研究会副理事长，云南省应用统计学会理事长。在 JASA、Annals of Statistics、Biometrika 等刊物发表学术论文 180 余篇，其中 SCI 检索 130 多篇。曾获“霍英东教育基金会第九届高等院校青年教师奖”，省部级科技奖励 9 项。

报告摘要：This paper is concerned with identifying important features in high dimensional data analysis, especially when there are complex relationships among predictors. Without any specification of an actual model, we first introduce a multiple testing procedure based on the quantile correlation to select important predictors in high dimensionality. The quantile-correlation statistic is able to capture a wide range of dependence. A stepwise procedure is studied for further identifying important variables. Moreover, a sure independent screening based on the quantile correlation is developed in handling ultrahigh dimensional data. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.