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 …

Multiple imputation provides a useful strategy for dealing with data sets that have missing
values. Instead of filling in a single value for each missing value, a multiple imputation …

Analyses of multivariate data are frequently hampered by missing values. Until recently, the
only missing-data methods available to most data analysts have been relatively ad1 hoc …

As the" father" of multiple imputation (MI), it gives me great pleasure to be able to comment
on this collection of contributions on MI. The nice review by Paul Zhang serves as an …

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 …

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 …

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

Educational researchers have become increasingly aware of the problems and biases
which can be caused by missing data. Significant advances have been made in the last 15 …

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 …