In this article we present analytic techniques for inference from a dataset in which missing
values have been replaced by predictive means derived from an imputation model. The …

Executive summary Several approaches for (multiple) imputation of multivariate data have
been proposed recently. Schafer (1997) presents a methodology to describe the data by an …

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 …

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

As a procedure for handling missing data, Multiple imputation consists of estimating the
missing data multiple times to create several complete versions of an incomplete data set …

An appealing feature of multiple imputation is the simplicity of the rules for combining the
multiple complete-data inferences into a final inference, the repeated-imputation inference …

Missing data is a problem that occurs frequently in many scientific areas. The most
sophisticated method for dealing with this problem is multiple imputation. Contrary to other …

Multiple imputation is a well-established general technique for analyzing data with missing
values. A convenient way to implement multiple imputation is sequential regression multiple …

This article describes and evaluates a procedure for imputing missing values for a relatively
complex data structure when the data are missing at random. The imputations are obtained …

We propose a remedy to the substantial discrepancy between the way political scientists
analyze data with missing values and the recommendations of the statistics community. With …