时 间： 2019年12月24日（周二）16：00-17：00
地 点： 中北校区理科大楼A1716报告厅
题 目：Integrating multi-source block-wise missing data in model selection
摘 要：For multi-source data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this paper, we propose a Multiple Block-wise Imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observa- tions. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms.
报告人简介： Fei Xue is a Postdoc Researcher under the supervision of Professor Hongzhe Li in Department of Biostatistics, Epidemiology and Informatics at University of Pennsylvania. She received her PhD degree in Statistics at University of Illinois Urbana-Champaign in 2019, advised by Professor Annie Qu. Prior to UIUC, she obtained her bachelor’s degree from School of Mathematical Sciences at Fudan University in 2014. Fei’s general research goal is to develop statistical methods for improving data integration, variable selection, and mediation analysis. She is also interested in missing data, high dimensional data, precision medicine, statistical genetics, and survival analysis.