Abstract
Predictive mean matching is an imputation method that combines parametric and nonparametric techniques. It imputes missing values by means of the Nearest Neighbor Donor with distance based on the expected values of the missing variables conditional on the observed covariates, instead of computing the distance directly on the values of the covariates. In ordinary predictive mean matching the expected values are computed through a linear regression model. In this paper a generalization of the original predictive mean matching is studied. Here the expected values used for computing the distance are estimated through an approach based on Gaussian mixture models. This approach includes as a special case the original predictive mean matching but allows one to deal also with nonlinear relationships among the variables. In order to assess its performance, an empirical evaluation based on simulations is carried out.
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Di Zio, M., Guarnera, U. Semiparametric predictive mean matching. AStA Adv Stat Anal 93, 175–186 (2009). https://doi.org/10.1007/s10182-008-0081-2
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DOI: https://doi.org/10.1007/s10182-008-0081-2