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

Bibliographic Data

ID19290097
AuthorsAntonio D’ambrosio (0000-0002-1905-037X, University of Naples Federico II, corresponding author), J Fernando Vera (0000-0002-6499-7132, Universidad de Granada), Willem J Heiser (Leiden University)
Year2022
Volume57
Issue4
Pages679-699
Publication date2022-07-04
Open AccessNo
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2021.1899892
PMID33843387
OpenAlexW3156141333
LanguageEN
References cited43

In this paper 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 geometrical space. Several applications on real data sets show that our procedure returns non-degenerate Unfolding solutions

Algorithm · Combinatorics · Data mining · Data set · Degenerate energy levels · Matrix (chemical analysis) · Missing data · Ordinal data · Ordinal regression · Physics · Position (finance) · Preference · Rank (graph theory) · Set (abstract data type) · Simple (philosophy) · Space (punctuation) · Statistics · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Multi-Criteria Decision Making · Theoretical Computer Science

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