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Activity Number: 112 - Methods for Imputing Missing Survey Data
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #322807 View Presentation
Title: Multiply Robust Nonparametric Multiple Imputation for the Treatment of Missing Data
Author(s): Sixia Chen* and David Haziza
Companies: and University of Montreal
Keywords: Double robustnes ; Missing data ; Multiple robustness ; Multiple imputation ; Variance estimation
Abstract:

Imputation is an effective way to handle missing values. In this paper, we propose a nonparametric multiple imputation procedure that makes use of multiple outcome regression models and/or multiple nonresponse models. Our procedure leads to a multiply robust point estimator in the sense that it remains consistent if anyone of these multiple models is correctly specified. A variance estimate is readily obtained by applying the customary rule advocated by Rubin (1987). The asymptotic properties of the proposed method are established. Results from a simulation study, assessing the proposed method in terms of bias, efficiency and coverage probability, support our findings.


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