Missing data in optimal scaling
Pier Alda Ferrari and
Paola Annoni
Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano
Abstract:
We propose a procedure to assess a measure for a latent phenomenon, starting from the observation of a wide set of ordinal variables affected by structured missing data. The proposal is based on Nonlinear PCA technique to be jointly used with an ad hoc imputation method for the treatment of missing data. The procedure is particularly suitable when dealing with ordinal, or mixed, variables, which are strongly interrelated and in the presence of specific patterns of missing observations
Keywords: Nonlinear PCA; monotone missing data; ordinal variables; missing data passive (search for similar items in EconPapers)
Date: 2005-01-01
New Economics Papers: this item is included in nep-ecm and nep-mkt
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://wp.demm.unimi.it/files/wp/2005/DEMM-2005_019wp.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:mil:wpdepa:2005-19
Access Statistics for this paper
More papers in Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano Via Conservatorio 7, I-20122 Milan - Italy. Contact information at EDIRC.
Bibliographic data for series maintained by DEMM Working Papers ( this e-mail address is bad, please contact ).