Abstract
Parametric imputation is widely used in missing data analysis. When the imputation model is misspecified, estimators based on parametric imputation are usually inconsistent. In this case, we propose to estimate and subtract off the asymptotic bias to obtain consistent estimators. Estimation of the bias involves modeling the missingness mechanism, and we allow multiple models for it. Our method simultaneously accommodates these models. The resulting estimator is consistent if any one of the missingness mechanism models or the imputation model is correctly specified.
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Han, P. (2016). Improving the Robustness of Parametric Imputation. In: Chen, DG., Chen, J., Lu, X., Yi, G., Yu, H. (eds) Advanced Statistical Methods in Data Science. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-2594-5_10
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DOI: https://doi.org/10.1007/978-981-10-2594-5_10
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