Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables

DOI10.1016/j.csda.2010.04.005zbMath1284.62068OpenAlexW2001388444WikidataQ35153257 ScholiaQ35153257MaRDI QIDQ2445680

Ian R. White, Patrick Royston, Rhian M. Daniel

Publication date: 14 April 2014

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2010.04.005

zbMATH Keywords

separationmissing datamultiple imputationperfect prediction

Mathematics Subject Classification ID

Related Items (5)

Missing data: A statistical framework for practice ⋮ Imputation strategies for clustering mixed-type data with missing values ⋮ An imputation method for categorical variables with application to nonlinear principal component analysis ⋮ Comparison of alternative imputation methods for ordinal data ⋮ A note on imputing squares via polynomial combination approach

Uses Software

• Stata
• SAS
• IVEware
• MICE
• mi
• SAS/STAT
• ice
• mvis

Cites Work

• A modified score function estimator for multinomial logistic regression in small samples.
• Bias reduction of maximum likelihood estimates
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• Multiple imputation of discrete and continuous data by fully conditional specification
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