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 …

The author presents the results of the recent decade in the theory of multiple imputation if
deficient data have to be analyzed. Multiple imputation is a generalization of the traditional …

Multiple imputation was first conceived as a tool that statistical agencies could use to handle
nonresponse in large-sample public use surveys. In the last two decades, the multiple …

Multiple imputation for non‐response replaces each missing value by two or more plausible
values. The values can be chosen to represent both uncertainty about the reasons for non …

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 …

Multiple imputation was designed to handle the problem of missing data in public-use data
bases where the data-base constructor and the ultimate user are distinct entities. The …

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 …

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 …

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 …

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