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A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances

Published online by Cambridge University Press:  01 January 2025

Antonio D’Ambrosio Open the ORCID record for Antonio D’Ambrosio [Opens in a new window]  and
Willem J. Heiser
Antonio D’Ambrosio*
Affiliation:
University of Naples Federico II
Willem J. Heiser
Affiliation:
Leiden University
*
Correspondence should be made to Antonio D’Ambrosio, Department of Economics and Statistics, University of Naples Federico II, Via Cinthia, 80126 Naples, Italy. Email: [email protected]
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Abstract

Preference rankings usually depend on the characteristics of both the individuals judging a set of objects and the objects being judged. This topic has been handled in the literature with log-linear representations of the generalized Bradley-Terry model and, recently, with distance-based tree models for rankings. A limitation of these approaches is that they only work with full rankings or with a pre-specified pattern governing the presence of ties, and/or they are based on quite strict distributional assumptions. To overcome these limitations, we propose a new prediction tree method for ranking data that is totally distribution-free. It combines Kemeny’s axiomatic approach to define a unique distance between rankings with the CART approach to find a stable prediction tree. Furthermore, our method is not limited by any particular design of the pattern of ties. The method is evaluated in an extensive full-factorial Monte Carlo study with a new simulation design.

Keywords

prediction treeskemeny distancepreference rankingsconsensus ranking
Type
Original Paper
Information
Psychometrika , Volume 81 , Issue 3 , September 2016 , pp. 774 - 794
Copyright
Copyright © 2016 The Psychometric Society

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