报告题目: Quantile correlation-based variable selection
时 间：2021年11月01日（星期一）下午15：00
地 点：腾讯会议（ID：450 554 189）
主 办：数学与统计学院
参加对象：统计系老师与学生
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 correlationto 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.
报告人简介：唐年胜，云南大学教授，博士生导师，数学与统计学院院长。“国家杰出青年科学基金”获得者，教育部“长江学者”特聘教授，教育部“新世纪优秀人才”，国家百千万人才工程暨有突出贡献中青年科学家，享受国务院特殊津贴。国际统计学会推荐会员，国际数理统计学会会士，在Journal of the American Statistical Association（美国统计学会会刊）、Annals of Statistics、Biomertrika等学术期刊发表论文170余篇，出版专著4部。