报告题目：Imputed factor regression for high-dimensional block-wise missing data

报 告 人：唐年胜 教授 云南大学

报告时间：2020年6月9日 15:00-16:00

报告地点：腾讯会议ID：859 354 444

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https://meeting.tencent.com/s/iXYOncNvJTVZ

校内联系人：程建华 chengjh@jlu.edu.cn

报告摘要：

Block-wise missing data are becoming increasingly common in high-dimensional biomedical, social, psychological, and environmental studies. As a result, we need efficient dimension-reduction methods for extracting important information for predictions under such data. Existing dimension-reduction methods and feature combinations are ineffective for handling block-wise missing data. We propose a factor-model imputation approach that targets block-wise missing data, and use an imputed factor regression for the dimension reduction and prediction. Specifically, we first perform screening to identify the important features. Then, we impute these features based on the factor model, and build a factor regression model to predict the response variable based on the imputed features. The proposed method utilizes the essential information from all observed data as a result of the factor structure of the model. Furthermore, the method remains efficient even when the proportion of block-wise missing is high. We show that the imputed factor regression model and its predictions are consistent under regularity conditions. We compare the proposed method with existing approaches using simulation studies, after which we apply it to data from the Alzheimer’s Disease Neuroimaging Initiative. Our numerical results confirm that the proposed method outperforms existing competitive approaches.

报告人简介：

唐年胜，云南大学二级教授、数学与统计学院经理、博士生导师。国家杰出青年科学基金获得者，入选教育部“新世纪优秀人才”计划、国家百千万人才工程，获得“国家有突出贡献中青年专家”荣誉称号，享受国务院政府特殊津贴；云南省科技领军人才、首批“云岭学者”和“省委联系专家”、中青年学术和技术带头人、云南省高等学校教学名师，云南省高校“统计与信息技术重点实验室”负责人，“云南大学复杂数据统计推断方法研究”省创新团队带头人；国际统计学会推选会员（Elected ISI Member），国际泛华统计学会理事会成员（Board of Directors）；2018年获ICSA杰出服务奖。主要从事统计诊断、非线性模型、生物医学统计等方面的研究，在国内外学术刊物发表论文150余篇，其中SCI检索120余篇；获得省部级科研奖励9项。