Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws

TL;DR

This paper introduces maximum likelihood multiple imputation (MLMI) as a faster, more efficient alternative to Bayesian posterior draw MI (PDMI), with new methods for consistent standard error estimation and implementation in R packages.

Contribution

It develops and evaluates three new standard error formulas for MLMI, enabling faster and more reliable multiple imputation without posterior draws.

Findings

MLMI is computationally faster than PDMI.

The proposed standard error formulas are consistent and applicable under MLMI.

Implementation in R packages facilitates practical use.

Abstract

Multiple imputation (MI) is a method for repairing and analyzing data with missing values. MI replaces missing values with a sample of random values drawn from an imputation model. The most popular form of MI, which we call posterior draw multiple imputation (PDMI), draws the parameters of the imputation model from a Bayesian posterior distribution. An alternative, which we call maximum likelihood multiple imputation (MLMI), estimates the parameters of the imputation model using maximum likelihood (or equivalent). Compared to PDMI, MLMI is less computationally intensive, faster, and yields slightly more efficient point estimates. A past barrier to using MLMI was the difficulty of estimating the standard errors of MLMI point estimates. We derive, implement, and evaluate three consistent standard error formulas: (1) one combines variances within and between the imputed datasets, (2) one uses the score function, and (3) one uses the bootstrap to estimate variance components due to sampling and imputation. Formula (1) modifies for MLMI a formula that has long been used under PDMI, while formulas (2) and (3) can be used without modification under either PDMI or MLMI. We have implemented MLMI and the standard error estimators in the mlmi and bootImpute packages for R.