Multiple imputation (MI) inference handles missing data by imputing the missing values m
times, and then combining the results from the m complete-data analyses. However, the …

Multivariate extensions of well-known linear mixed-effects models have been increasingly
utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The …

Currently, a growing number of programs become available in statistical software for
multiple imputation of missing values. Among others, two algorithms are mainly …

Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most
common approaches to missing data analysis. In theory, MI and FIML are equivalent when …

Multiple imputation is a popular method for addressing data that are presumed to be missing
at random. To obtain accurate results, one's imputation model must be congenial to …

Background Multiple imputation is a recommended method to handle missing data. For
significance testing after multiple imputation, Rubin's Rules (RR) are easily applied to pool …

With the advent of general purpose packages that support multiple imputation for analyzing
datasets with missing data (eg, Solas, SAS PROC MI, and S-Plus 6.0), we expect much …

This paper considers the problem of imputation model uncertainty in the context of missing
data problems. We argue that so-called “Bayesianly proper” approaches to multiple …

In missing data analysis, the reporting of missing rates is insufficient for the readers to
determine the impact of missing data on the efficiency of parameter estimates. A more …

The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …