Economics at your fingertips  

A Proposal for Setting-up Indicators in the Presence of Missing Data: the Case of Vulnerability Indicators

Pier Alda Ferrari (), Paola Annoni and Sergio Urbisci

No unimi-1002, UNIMI - Research Papers in Economics, Business, and Statistics from Universitá degli Studi di Milano

Abstract: A procedure for the construction of an indicator in the presence of structured missing data is proposed. In particular, we face the problem of creating a ‘measure’ of the damage degree of valuable historical-architectonical buildings on the basis of the observation of several ordinal variables. Our proposal is the jointly use of Nonlinear PCA and an imputation method for missing data treatment. The adopted procedure can be generally applied when an indicator is needed on the basis of the observation of ordinal, but also nominal or numerical, variables, which are deeply interrelated and are affected by systematic missing data. It has the nice feature of treating missing data according to the relevance of variables affected by missing observations and, at the same time, it preserves all the properties of Nonlinear PCA without missing data. Furthermore, the method provides category quantifications and variable loadings that could be used for future inventory of buildings (in general of ‘units’) not included in the initial survey.

Keywords: nonlinear MVA; quantification of ordinal variables; optimal scaling; measurement. (search for similar items in EconPapers)
Date: 2005-04-26
Note: oai:cdlib1:unimi-1002
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (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:

Access Statistics for this paper

More papers in UNIMI - Research Papers in Economics, Business, and Statistics from Universitá degli Studi di Milano Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().

Page updated 2019-05-15
Handle: RePEc:bep:unimip:unimi-1002