Missingness is a commonly occurring phenomenon in many applications. Determining a
suitable analytical approach in the absence of complete observations is a major focus of …

In real-life situations, we often encounter data sets containing missing observations.
Statistical methods that address missingness have been extensively studied in recent years …

Missing data are frequently encountered in practice. A broader class of missing data is
called incomplete data, which includes data with measurement error, multilevel data with …

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 …

Missing data are a common problem in statistics. Imputation, or filling in the missing values,
is an intuitive and flexible way to address the resulting incomplete data sets. We focus on …

The treatment of missing data has been an issue in statistics for some time, but it has come
to the forefront in recent years because of improved techniques for dealing with the problem …

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

In the last decade, substantial progress has been made on methods for imputation of
missing data. Modern imputation methods have become widely available for practitioners …

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 …