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).
Copula-Based Non-Metric Unfolding on Augmented Data Matrix / Nai Ruscone, Marta; D'Ambrosio, Antonio. - (2019), pp. 357-360. (Intervento presentato al convegno 12th Scientific Meeting of the Classification and Data Analysis Group (CLADAG 2019) tenutosi a Cassino nel 11-13 settembre 2019).
Copula-Based Non-Metric Unfolding on Augmented Data Matrix
Antonio D'Ambrosio
2019
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
