伟德BETVlCTOR1946建院40周年系列活动之学术讲座第33期
关于名师讲座第11期
主题: Imputed factor regression for high-dimensional block-wise missing data
主讲人: 唐年胜 (云南大学)
主持人：郑贤副院长
地点：伟德BETVlCTOR1946（中惠楼）102 室
会议时间：2020年11月6日下午4:00-5:00
摘要
Block-wise missing data are becoming increasingly common in highdimensional 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.
主讲人简介
唐年胜，云南大学二级教授、数学与统计学院院长、博士生导师。国家杰出青年科学基金获得者，教育部长江学者特聘教授，国家百千万人才工程入选者，国家有突出贡献中青年专家，享受国务院政府特殊津贴，教育部新世纪优秀人才支持计划获得者，云南省科技领军人才，国际统计学会推选会员，国际泛华统计学会“Board of Directors”，云南省高等学校教学名师。在JASA、Annals of Statistics、Biometrika等刊物发表学术论文170余篇，其中SCI检索127篇。曾获“霍英东教育基金会第九届高等院校青年教师奖”，省部级科技奖励9项。
