Norm 2.03

Multiple Imputation with Norm 2.03. Multiple imputation is a simulation-based approach to the statistical analysis of incomplete data. In multiple imputation, each missing datum is replaced by m>1 simulated values. The resulting m versions of the complete data can then be analyzed by standard complete-data methods, and the results combined to produce inferential statements (e.g. interval estimates or p-values) that incorporate missing-data uncertainty.

References in zbMATH (referenced in 1 article )

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  1. Graham, John W.: Missing data. Analysis and design (2012)