Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415086 | Computational Statistics & Data Analysis | 2011 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Pier Alda Ferrari, Paola Annoni, Alessandro Barbiero, Giancarlo Manzi,