北京科学与工程计算研究院学术报告之五十四

报告人/Speaker: 王启华,中国科学院数学与系统科学研究院


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


时间/Date & Time: May 30, 2018, 15:30—16:30


地点/Venue:北京科学与工程计算研究院M843会议室


报告摘要/Abstract

It is quite challenge 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. This paper develops 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.


报告人简介/About the speaker:

王启华,中国科学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,国际统计研究会当选会员(elected member), 先后访问加拿大Carleton大学、California大学戴维斯分校、California大学洛杉矶分校、美国Yale大学、美国华盛顿大学、美国西北大学、德国Humboldt大学、澳大利亚国立大学及澳大利亚悉尼大学等。主要从事生存分析、缺失数据分析、高维数据统计分析及非-半参数统计推断等方面的研究。出版专著两部,发表论文百余篇,其中90多篇发表在 The Annals of Statistics,  JASA及Biometrika等国际重要刊物,2014,2015,2016及2017连续4年被Elsevier列入中国高被引专家, 是一些国际与国内刊物的主编与编委。