Title: An imputation method for categorical variables with application to nonlinear principal component analysis
Authors: FERRARI Pier AldaANNONI PaolaBARBIERO AlessandroMANZI Giancarlo
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS vol. 55 no. 7 p. 2410-2420
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2011
JRC N°: JRC56769
ISSN: 0167-9473
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC56769
DOI: 10.1016/j.csda.2011.02.007
Type: Articles in Journals
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 R.
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