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Missing data in optimal scaling

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  • Pieralda FERRARI
  • Paola ANNONI

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

Suggested Citation

  • Pieralda FERRARI & Paola ANNONI, 2005. "Missing data in optimal scaling," Departmental Working Papers 2005-19, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2005-19
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    File URL: http://wp.demm.unimi.it/files/wp/2005/DEMM-2005_019wp.pdf
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    References listed on IDEAS

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    1. Pieralda Ferrari & Paola Annoni & Sergio Urbisci, 2005. "A Proposal for Setting-up Indicators in the Presence of Missing Data: the Case of Vulnerability Indicators," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1002, Universitá degli Studi di Milano.
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      Keywords

      Nonlinear PCA; monotone missing data; ordinal variables; missing data passive;
      All these keywords.

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