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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| Veröffentlicht in: | Journal of classification 2024-11, Vol.41 (3), p.678-697 |
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| container_title | Journal of classification |
| container_volume | 41 |
| creator | Nai Ruscone, Marta Fernández, Daniel D’Ambrosio, Antonio |
| description | 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. |
| doi_str_mv | 10.1007/s00357-024-09495-x |
| format | Article |
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| language | eng |
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| source | SpringerNature Journals |
| subjects | Bioinformatics Data augmentation Marketing Mathematics and Statistics Pattern Recognition Psychometrics Signal,Image and Speech Processing Statistical Theory and Methods Statistics |
| title | Copula-Based Non-Metric Unfolding on Augmented Data Matrix |
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