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Missing Data in Optimal Scaling

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Data Analysis, Classification and the Forward Search

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 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.

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References

  • FERRARI, P.A., ANNONI, P. and URBISCI, S. (2005): A proposal for setting-up indicators in the presence of missing data: the case of vulnerability indicators. UNIMI-Research Papers in Economies, Business, and Statistics. Statistics and Mathematics. Working Paper 1. http://services.bepress.com/unimi/statistics/artl1..

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Author information

Authors and Affiliations

  1. Dep. of Economics. Business and Statistics, University of Milan, Italy

    Pier Alda Ferrari & Paola Annoni

Authors
  1. Pier Alda Ferrari
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  2. Paola Annoni
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Editor information

Editors and Affiliations

  1. Department of Economics Section of Statistics and Computing, University of Parma, Via Kennedy 6, 43100, Parma, Italy

    Sergio Zani , Andrea Cerioli  & Marco Riani ,  & 

  2. Department of Statistics, Probability and Applied Statistics, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185, Roma, Italy

    Maurizio Vichi

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© 2006 Springer-Verlag Heidelberg

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Ferrari, P.A., Annoni, P. (2006). Missing Data in Optimal Scaling. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_10

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