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

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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.

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Acknowledgments

The authors would like to thank anonymous reviewers for their helpful comments, which have helped us to greatly improve the quality of this manuscript.

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Authors and Affiliations

  1. Department of Economics and Statistics, University of Naples Federico II, Via Cinthia, 80126 , Naples, Italy

    Antonio D’Ambrosio

  2. Mathematical Institute and Institute of Psychology, Leiden University, Leiden, The Netherlands

    Willem J. Heiser

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  1. Antonio D’Ambrosio
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  2. Willem J. Heiser
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Correspondence to Antonio D’Ambrosio.

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D’Ambrosio, A., Heiser, W.J. A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances. Psychometrika 81, 774–794 (2016). https://doi.org/10.1007/s11336-016-9505-1

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  • DOI: https://doi.org/10.1007/s11336-016-9505-1

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