報告題目：Imputed factor regression for high-dimensional block-wise missing data
報 告 人：唐年勝 教授 雲南大學
報告時間：2020年6月9日 15:00-16:00
報告地點：騰訊會議ID：859 354 444
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校内聯系人：程建華 chengjh@jlu.edu.cn
報告摘要：
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.
報告人簡介：
唐年勝，雲南大學二級教授、數學與統計學院院長、博士生導師。國家傑出青年科學基金獲得者，入選教育部“新世紀優秀人才”計劃、國家百千萬人才工程，獲得“國家有突出貢獻中青年專家”榮譽稱号，享受國務院政府特殊津貼；雲南省科技領軍人才、首批“雲嶺學者”和“省委聯系專家”、中青年學術和技術帶頭人、雲南省高等學校教學名師，雲南省高校“統計與信息技術重點實驗室”負責人，“雲南大學複雜數據統計推斷方法研究”省創新團隊帶頭人；國際統計學會推選會員（Elected ISI Member），國際泛華統計學會理事會成員（Board of Directors）；2018年獲ICSA傑出服務獎。主要從事統計診斷、非線性模型、生物醫學統計等方面的研究，在國内外學術刊物發表論文150餘篇，其中SCI檢索120餘篇；獲得省部級科研獎勵9項。
