Daniel Fernández is a Serra-Húnter Fellow, a member of the Centro de Investigación Biomédica en Red de Salud Mental (Instituto de Salud Carlos III), and his work has been supported by the Ministerio de Ciencia e Innovación y Universidades (Spain) [PID2023-148033OB-C21], and by grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain). The research work of Antonio D’Ambrosio is supported by the European Union - NextGenerationEU, in the framework of the ECSC - Centro Nazionale HPC, Big Data e Quantum Comput-ing (ECSC E63C22000980007 – CUP CN_00000013).

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.

Peer Reviewed