An imputation method for categorical variables with application to nonlinear principal component analysis
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Pier Alda Ferrari & Paola Annoni & Giancarlo Manzi, 2007.
"Evaluation and comparison of European countries: public opinion on services,"
UNIMI - Research Papers in Economics, Business, and Statistics
unimi-1058, Universitá degli Studi di Milano.
- Pier Ferrari & Paola Annoni & Giancarlo Manzi, 2010. "Evaluation and comparison of European countries: public opinion on services," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(6), pages 1191-1205, October.
- Serneels, Sven & Verdonck, Tim, 2009. "Principal component regression for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3855-3863, September.
- James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584.
- Siddique, Juned & Belin, Thomas R., 2008. "Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 405-415, December.
- Christopher Paul & William Mason & Daniel McCaffrey & Sarah Fox, 2008. "A cautionary case study of approaches to the treatment of missing data," Statistical Methods and Applications, Springer, vol. 17(3), pages 351-372, July.
- White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:55:y:2011:i:7:p:2410-2420. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.