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

Antonio D’Ambrosio () and Willem J. Heiser
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Antonio D’Ambrosio: University of Naples Federico II
Willem J. Heiser: Leiden University

Psychometrika, 2016, vol. 81, issue 3, No 9, 774-794

Abstract: 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 trees; kemeny distance; preference rankings; consensus ranking (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s11336-016-9505-1

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