10月9日 王启华教授学术报告(数统学院)

文章作者:  发布时间: 2017-10-08  浏览次数: 10

报 告 人:王启华

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

报告时间:2017年10月9日(周一)上午10:00

报告地点:静远楼1506报告厅

主办单位:数学与统计学院、科技处

报告人简介:

    王启华,中国科学院数学与系统科学研究院研究员,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), 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及2016连续3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。

报告摘要:

    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 {\it 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.