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Dimensionality Reduction Methods for Contingency Tables with Ordinal Variables

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Studies in Theoretical and Applied Statistics (SIS 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 227))

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Abstract

Several extensions of correspondence analysis have been introduced in literature coping with the possible ordinal structure of the variables. They usually obtain a graphical representation of the interdependence between the rows and columns of a contingency table, by using several tools for the dimensionality reduction of the involved spaces. These tools are able to enrich the interpretation of the graphical planes, providing also additional information, with respect to the usual singular value decomposition. The main aim of this paper is to suggest an unified theoretical framework of several methods of correspondence analysis coping with ordinal variables.

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Author information

Authors and Affiliations

  1. Department of Economics, Management and Institutions, University “Federico II” of Naples, Via Cinthia Monte Sant’Angelo, Naples, Italy

    Luigi D’Ambra

  2. Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Piazza Arechi II, Benevento, Italy

    Pietro Amenta

  3. Department of Economics, University of Campania “L.Vanvitelli”, Corso Gran Priorato di Malta, Capua, Italy

    Antonello D’Ambra

Authors
  1. Luigi D’Ambra
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  2. Pietro Amenta
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  3. Antonello D’Ambra
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Corresponding author

Correspondence to Luigi D’Ambra .

Editor information

Editors and Affiliations

  1. Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno, Fisciano, Salerno, Italy

    Cira Perna

  2. Dipartimento di Economia e Management, Università degli Studi di Pisa, Pisa, Italy

    Monica Pratesi

  3. Toulouse School of Economics, University of Toulouse, Toulouse Cedex 6, France

    Anne Ruiz-Gazen

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D’Ambra, L., Amenta, P., D’Ambra, A. (2018). Dimensionality Reduction Methods for Contingency Tables with Ordinal Variables. In: Perna, C., Pratesi, M., Ruiz-Gazen, A. (eds) Studies in Theoretical and Applied Statistics. SIS 2016. Springer Proceedings in Mathematics & Statistics, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73906-9_13

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