Multiple imputation: review of theory, implementation and software

By Ofer Harel, Xiao-Hua Zhou. Statistics in Medicine, 2007

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Multiple imputation using chained equations: Issues and guidance for practice (2011, methodology): “Multiple imputation (MI) [2] is a statistical technique for handling missing data, which has become increasingly popular because of its generality and recent software developments [3, 4].”
Multiple Imputation by Chained Equations: What is it and how does it work? (2011, background): “M 46 in Graham (2009), Harel and Zhou (2007), Horton and Kleinman (2007) and Yu et al. (2007).”
Missing data techniques for multilevel data: implications of model misspecification (2011, methodology): “In the past decade, information regarding the principled MD methods of ML and MI has become increasingly available to researchers in the social sciences, as have software packages designed to perform these techniques 11 31 .”
Flexible Imputation of Missing Data (2012, methodology): “Mixed continuous-categorical data can be imputed under the general location model originally developed by Olkin and Tate (1961). This model combines the log-linear and multivariate normal models by fitting a restricted normal model to each cell of the contingency table.”
The estimation of R 2 and adjusted R 2 in incomplete data sets using multiple imputation (2009, methodology): “The main application reference Schafer [25] and reviews [1,14,23,27] have been published.”