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题 目：Imputed factor regression for high-dimensional block-wise missing data
主讲人：唐年胜教授 “国家杰青 ”、“长江学者”、云南大学数学与外围投注平台推荐院长
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.
唐年胜，博士，国家杰出青年科学基金获得者，教育部“长江学者”特聘教授，教育部“新世纪优秀人才”，云南省科技领军人才，云南省首批云岭学者，云南省中青年学术和技术带头人，云南省教学名师，云南省学位委员会经济与管理学科评议组成员，博士生导师。 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。