Multiple Imputation for Model Checking: Completed‐Data Plots with Missing and Latent Data

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Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There is a large literature on testing the validity of the assumptions made in multiple imputation, as well as a variety of sensitivity analyses (Little and Rubin 1987; Robins et al. 1999; Scharfstein et al. 1999; Gelman et al. 2005; Carpenter et al. 2007; NAP 2010).”
Using Calibration to Improve Rounding in Imputation (2008, methodology): “[A similar approach is taken by Gelman et al. (2005), but with a slightly different handling of the originally missing values of Y .]”
Multiple Imputation of Missing or Faulty Values Under Linear Constraints (2014, background): “As described in Gelman et al. (2005), one can compare the marginal distributions of the observed and imputed values as a “sanity check.””
Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box (2011, methodology): “Thus we suggest that users should check the t of the conditional models by looking at several diagnostic plots before running a longer imputation procedure ( Gelman, Van Mechelen, Verbeke, Heitjan, and Meulders 2005; Abayomi, Gelman, and Levy 2008).”