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An imputation method for categorical variables with application to nonlinear principal component analysis

Pier Alda Ferrari (), Paola Annoni, Alessandro Barbiero () and Giancarlo Manzi ()

Computational Statistics & Data Analysis, 2011, vol. 55, issue 7, 2410-2420

Abstract: The 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.

Keywords: Composite; indicators; Forward; imputation; Imputation; procedure; Listwise; deletion; Nearest; neighbor; Ordinal; data; Passive; treatment (search for similar items in EconPapers)
Date: 2011
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Handle: RePEc:eee:csdana:v:55:y:2011:i:7:p:2410-2420