Investigating the performance of a variation of multiple correspondence analysis for multiple imputation in categorical data sets

DOI10.1007/S00357-017-9238-6zbMath1381.62278OpenAlexW2761739857MaRDI QIDQ1695091

Michael J. von Maltitz, Johané Nienkemper-Swanepoel

Publication date: 6 February 2018

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-017-9238-6

zbMATH Keywords

principal component analysismultiple imputationmultiple correspondence analysisincomplete categorical dataregularized iterative multiple correspondence analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (1)

Investigating the performance of a variation of multiple correspondence analysis for multiple imputation in categorical data sets

Uses Software

• R
• MICE

Cites Work

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• Handling missing values with regularized iterative multiple correspondence analysis
• Investigating the performance of a variation of multiple correspondence analysis for multiple imputation in categorical data sets
• MULTIPLE IMPUTATION COMPARED WITH SOME INFORMATIVE DROPOUT PROCEDURES IN THE ESTIMATION AND COMPARISON OF RATES OF CHANGE IN LONGITUDINAL CLINICAL TRIALS WITH DROPOUTS
• Multiple Imputation: Theory and Method
• Multiple imputation: current perspectives

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