Is multiple imputation like EM?
MI bears a close resemblance to the EM algorithm and other computational methods for calculating maximum-likelihood estimates based on the observed data alone. These methods summarize a likelihood function which has been averaged over a predictive distribution for the missing values. MI performs this same type of averaging by Monte Carlo rather than by numerical methods. In large samples, when relevant aspects of the imputer’s and analyst’s models agree, inferences obtained by MI with sufficiently many imputations will be nearly the same as those obtained by direct maximization of the likelihood.
