COMPSTAT 2022: Start Registration
View Submission - COMPSTAT2022
Title: Copula-based non-metric unfolding Authors:  Marta Nai Ruscone - Università degli Studi di Genova (Italy) [presenting]
Antonio Dambrosio - Universita di Napoli Federico II (Italy)
Daniel Fernandez - Universitat Politecnica de Catalunya, BarcelonaTech (UPC) (Spain)
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 Copulas-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 an acceptable recovery of given preference structures. Applications on real datasets show that our procedure returns non-degenerate unfolding solutions.