: A simple but effective procedure to avoid degeneracies in ordinal Unfolding for preference rank data based on the Kemeny distance is proposed. Considering Unfolding as a particular MDS procedure with missing within-set proximities, unknown proximities are first estimated using correlations related to the Kemeny distance, and then the complete proximity matrix is analyzed in a standard MDS framework. A simulation study shows that our proposal is able to both recover the order of the preferences and reproduce the position of both rankings and objects in a reduced geometrical space

Avoiding Degeneracies in Ordinal Unfolding Using Kemeny-Equivalent Dissimilarities for Two-Way Two-Mode Preference Rank Data / D'Ambrosio, Antonio. - (2022). (Intervento presentato al convegno INTERNATIONAL SYMPOSIUM ON NONPARAMETRIC STATISTICS tenutosi a Paphos - Cipro nel 20-24 Giugno 2022).

Avoiding Degeneracies in Ordinal Unfolding Using Kemeny-Equivalent Dissimilarities for Two-Way Two-Mode Preference Rank Data

Antonio D'Ambrosio
2022

Abstract

: A simple but effective procedure to avoid degeneracies in ordinal Unfolding for preference rank data based on the Kemeny distance is proposed. Considering Unfolding as a particular MDS procedure with missing within-set proximities, unknown proximities are first estimated using correlations related to the Kemeny distance, and then the complete proximity matrix is analyzed in a standard MDS framework. A simulation study shows that our proposal is able to both recover the order of the preferences and reproduce the position of both rankings and objects in a reduced geometrical space
2022
Avoiding Degeneracies in Ordinal Unfolding Using Kemeny-Equivalent Dissimilarities for Two-Way Two-Mode Preference Rank Data / D'Ambrosio, Antonio. - (2022). (Intervento presentato al convegno INTERNATIONAL SYMPOSIUM ON NONPARAMETRIC STATISTICS tenutosi a Paphos - Cipro nel 20-24 Giugno 2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/889454
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