講座主題：How to make model-free feature screening approaches for full data applicable to the case of missing response
專家姓名：王啟華
工作單位：中國(guó)科學(xué)院
講座時(shí)間：2018年3月21日14:00
講座地點(diǎn)：數(shù)學(xué)院大會(huì)議室
主辦單位：煙臺(tái)大學(xué)數(shù)學(xué)與信息科學(xué)學(xué)院
內(nèi)容摘要：
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 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.
主講人介紹：
王啟華,，中國(guó)科學(xué)院核心骨干特聘研究員,，博士生導(dǎo)師，國(guó)家杰出青年基金獲得者,，教育部長(zhǎng)江學(xué)者獎(jiǎng)勵(lì)計(jì)劃特聘教授，中科院“百人計(jì)劃”入選者,，國(guó)際統(tǒng)計(jì)研究會(huì)當(dāng)選會(huì)員（elected member）, 先后訪問(wèn)加拿大Carleton大學(xué),、California大學(xué)戴維斯分校、California大學(xué)洛杉磯分校,、美國(guó)Yale大學(xué),、美國(guó)華盛頓大學(xué)、美國(guó)西北大學(xué),、德國(guó)Humboldt大學(xué),、澳大利亞國(guó)立大學(xué)及澳大利亞悉尼大學(xué)等。主要從事生存分析,、缺失數(shù)據(jù)分析,、高維數(shù)據(jù)統(tǒng)計(jì)分析及非-半?yún)?shù)統(tǒng)計(jì)推斷等方面的研究。出版專著兩部,，發(fā)表論文百余篇,，其中90多篇發(fā)表在 The Annals of Statistics, JASA及Biometrika等國(guó)際重要刊物， 2014,、2015,、2016與2017連續(xù)4年被Elsevier列入中國(guó)高被引學(xué)者榜單, 是一些國(guó)際與國(guó)內(nèi)刊物的主編與編委,。
