Missing data is a common complication in data analysis. In many medical settings missing
data can cause difficulties in estimation, precision and inference. Multiple imputation …

It is now widely accepted that multiple imputation (MI) methods properly handle the
uncertainty of missing data over single imputation methods. Several standard statistical …

Multiple imputation (MI) is now well established as a flexible, general, method for the
analysis of data sets with missing values. Most implementations assume the missing data …

Multiple imputation: a primer - Joseph L Schafer, 1999 Skip to main content Intended for
healthcare professionals Sage Journals Home Search this journal Search all journals Enter …

Multiple imputation for nonresponse in public-use files replaces each missing value by two
or more plausible values. The values can be chosen to represent both uncertainty about …

Gary King, James Honaker, Anne Joseph, and Kenneth Scheve.“Analyzing Incomplete
Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political …

Statistical analysis with missing data has always been a challenge. However, there have
been tremendous advances in statistical theory related to analysis with missing data. Of …

Multiple imputation provides a useful strategy for dealing with data sets with missing values.
Instead of filling in a single value for each missing value, Rubin's (1987) multiple imputation …

This article provides a comprehensive review of multiple imputation (MI), a technique for
analyzing data sets with missing values. Formally, MI is the process of replacing each …

Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …