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Activity Number: 509 - Statistical Methodology
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #304994
Title: Integrating Multi-Source Block-Wise Missing Data in Model Selection
Author(s): Fei Xue* and Annie Qu
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: ADNI; Data integration; Dimension reduction; Generalized method of moments; Informative missing; Missing at random

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 observations. 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 optimally integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under fixed-dimensional and high-dimensional settings. Also, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation only. Moreover, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing methods under various missing mechanisms.

Authors who are presenting talks have a * after their name.

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