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Copula-Based Non-Metric Unfolding on Augmented Data Matrix

Author

Listed:
  • Marta Nai Ruscone

    (University of Genoa)

  • Daniel Fernández

    (Universitat Politècnica de Catalunya, BarcelonaTech (UPC))

  • Antonio D’Ambrosio

    (University of Naples Federico II)

Abstract

A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed. We adopt the strategy of augmenting the data matrix, trying to build a complete dissimilarity matrix, by using copula-based association measures among rankings (the individuals), and between rankings and objects (namely, a rank-order representation of the objects through tied rankings). The proposed technique leads to acceptable recovery of given preference structures.

Suggested Citation

  • Marta Nai Ruscone & Daniel Fernández & Antonio D’Ambrosio, 2024. "Copula-Based Non-Metric Unfolding on Augmented Data Matrix," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 678-697, November.
    • Handle: RePEc:spr:jclass:v:41:y:2024:i:3:d:10.1007_s00357-024-09495-x
    DOI: 10.1007/s00357-024-09495-x
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    References listed on IDEAS

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