Please use this identifier to cite or link to this item:
|Title:||An imputation method for categorical variables with application to nonlinear principal component analysis|
|Authors:||FERRARI Pier Alda; ANNONI Paola; BARBIERO Alessandro; MANZI Giancarlo|
|Citation:||COMPUTATIONAL STATISTICS & DATA ANALYSIS vol. 55 no. 7 p. 2410-2420|
|Publisher:||ELSEVIER SCIENCE BV|
|Type:||Articles in periodicals and books|
|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.|
|JRC Institute:||Space, Security and Migration|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.