报告题目： How to make model-free feature screening approaches for full data applicable to the case of  missing response

报告人：王启华研究员

报告摘要：It is quite challenging to develop model-free feature screening approaches for missing response problems since the existing standard missing data analysis methods cannot be applied directly to high dimensional case. In this talk, we propose some novel methods by borrowing information of missingness indicators such that any feature screening procedures for ultrahigh-dimensional covariates with full data can be applied to missing response case. The first method is the so-called missing indicator imputation screening, which is developed by proving that the set of the active predictors of interest for the response is a subset of the active predictors for the product of the response and missingness indicator under some mild conditions. As an alternative, another method called Venn diagram based approach is also developed. The sure screening property is proven for both methods. It is shown that the complete case analysis can also keep the sure screening property of any feature screening approach with sure screening property.

报告时间：12月18日下午15:30—16:30

报告地点：科技楼二楼北会议室

报告人简介：王启华，中国科学院数学与系统科学研究院核心骨干特聘研究员，博士生导师，国家杰出青年基金获得者，教育部长江学者奖励计划特聘教授，中科院“百人计划”入选者，首届全国百篇优秀博士论文获得者，钟家庆数学奖获得者，国际统计研究会推选会员（ISI Elected member）。2014，2015及2016连续3年入选Elsevier中国高被引学者。

他的主要研究方向为生存分析、经验似然、缺失数据、高维数据推断等。迄今为止，出版专著两部，发表学术论文百余篇，其中90多篇发表在 The Annals of Statistics, JASA，Biometrika等国际重要刊物。多次应邀访问国际著名高校，例如，California大学洛杉矶分校、美国Yale大学、美国华盛顿大学、澳大利亚国立大学、加拿大Carleton大学、California大学戴维斯分校等。