Copula-Based Non-Metric Unfolding on Augmented Data Matrix

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 mea...

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Bibliographic Details
Published in:Journal of classification 2024, Vol.41 (3), p.678-697
Main Authors: Nai Ruscone, Marta, Fernández, Daniel, D’Ambrosio, Antonio
Format: Article
Language:English
Subjects:
Bioinformatics
Data augmentation
Marketing
Mathematics and Statistics
Pattern Recognition
Psychometrics
Signal,Image and Speech Processing
Statistical Theory and Methods
Statistics
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Summary: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.
ISSN:0176-4268
1432-1343
DOI:10.1007/s00357-024-09495-x