报告题目：Sample-wise Combined Missing Effect Models with Penalization
报 告 人：李启寨 研究员 中国科学院数学与系统科学研究院
报告时间：2021年11月5日 下午 14:00-15:00
报告地点：腾讯会议 ID：553 620 445
或点击链接直接加入会议https://meeting.tencent.com/dm/TdDOpyJrEmHR
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：Modern high-dimensional statistical inference often faces the problem of missing data. In recent decades, many studies have focused on this topic and provided strategies including complete-sample analysis and imputation procedures. However, complete-sample analysis discards information of incomplete samples, while imputation procedures have accumulative errors from each single imputation. In this paper, we propose a new method, Sample-wise COmbined missing effect Models with penalization (SCOM), to deal with missing data occurring in predictors. Instead of imputing the predictors, SCOM estimates the combined effect caused by all missing data for each incomplete sample. SCOM makes full use of all available data and is robust with respect to various missing mechanisms. Theoretical studies show the oracle inequality for the proposed estimator, and the consistency of variable selection and combined missing effect selection. Simulation studies and an application to the Residential Building Data also illustrate its effectiveness.
报告人简介：李启寨，中国科学院数学与系统科学研究院 研究员， 2001年本科毕业于中国科技大学，2006年博士毕业于中国科学院研究生院（培养单位：中国科学院数学与系统科学研究院）；研究方向：生物医学统计等；发表及接收发表论文110余篇；现任中国数学会常务理事、全国工业统计学教学研究会常务理事等。曾主持国家自然科学基金委 优秀 、面上和青年项目；曾获美国统计学会会士 (ASA Fellow, 2020)，国际统计学会推选会员(ISI Elected Member, 2016)；农业部/中国农学会神农中华农业科技奖一等奖(2019)，中国工业与应用数学学会优秀青年学者奖(2015)，中国科学院卢嘉锡青年人才奖(2011)等。
