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Abstract:
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Most imputation techniques are designed for ignorable missing data mechanisms since nonignorability is an assumption more challenging to handle. Under nonignorable missingness (NM), one assumes the nonresponse mechanism depends on unobserved values, and the outcome model for the variables with missing values and the nonresponse model must be modeled jointly. Consequently, joint modeling can produce results that are sensitive to misspecification of the outcome and nonresponse models. We propose a nonparametric method for handling NM via bootstrap imputation and multiple imputation (MI). The key idea underlying our proposed approach is to formulate two working models for the outcome and for nonresponse. Using the two working models, we derive predictive scores which achieves dimension reduction and use the resulting scores coupled with a nearest neighbor hot deck to multiply impute missing values. Our approach allows users to incorporate prior knowledge on the working models through the use of weights. Compared with the existing MI methods, our approach is more robust to misspecification of the two models and allows for a natural sensitivity analysis.
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