MULTIPLY ROBUST NONPARAMETRIC MULTIPLE IMPUTATION FOR THE TREATMENT OF MISSING DATA

Imputation offers an effective solution to the problem of missing values. We propose a nonparametric multiple imputation procedure that uses multiple outcome regression models and/or multiple propensity score models. Our procedure leads to a multiply robust point estimator in the sense that it remai...

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Bibliographic Details
Published in:Statistica Sinica 2019-01, Vol.29 (4), p.2035-2053
Main Authors: Chen, Sixia, Haziza, David
Format: Article
Language:English
Online Access:Get full text
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Summary:Imputation offers an effective solution to the problem of missing values. We propose a nonparametric multiple imputation procedure that uses multiple outcome regression models and/or multiple propensity score models. Our procedure leads to a multiply robust point estimator in the sense that it remains consistent if all models but one are misspecified. We obtain a variance estimator and establish the asymptotic properties of the proposed method. The results of a simulation study, that assesses the proposed method in terms of bias, efficiency, and coverage probability, support our findings.
ISSN:1017-0405
1996-8507
DOI:10.5705/ss.202017.0126