An imputation method for categorical variables with application to nonlinear principal component analysis
AbstractThe problem of missing data in building multidimensional composite indicators is a delicate problem which is often underrated. An imputation method particularly suitable for categorical data is proposed. This method is discussed in detail in the framework of nonlinear principal component analysis and compared to other missing data treatments which are commonly used in this analysis. Its performance vs. these other methods is evaluated throughout a simulation procedure performed on both an artificial case, varying the experimental conditions, and a real case. The proposed procedure is implemented using R1.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 7 (July)
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Web page: http://www.elsevier.com/locate/csda
Composite indicators Forward imputation Imputation procedure Listwise deletion Nearest neighbor Ordinal data Passive treatment;
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- Siddique, Juned & Belin, Thomas R., 2008. "Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(2), pages 405-415, December.
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