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

Author

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  • Antonio D’Ambrosio

    (University of Naples Federico II)

  • Willem J. Heiser

    (Leiden University)

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.

Suggested Citation

  • Antonio D’Ambrosio & Willem J. Heiser, 2016. "A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 774-794, September.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:3:d:10.1007_s11336-016-9505-1
    DOI: 10.1007/s11336-016-9505-1
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    Cited by:

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    2. Antonella Plaia & Simona Buscemi & Mariangela Sciandra, 2021. "Consensus among preference rankings: a new weighted correlation coefficient for linear and weak orderings," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 1015-1037, December.
    3. Cascón, J.M. & González-Arteaga, T. & de Andrés Calle, R., 2022. "A new preference classification approach: The λ-dissensus cluster algorithm," Omega, Elsevier, vol. 111(C).
    4. Adolfo Morrone & Alfonso Piscitelli & Antonio D’Ambrosio, 2019. "How Disadvantages Shape Life Satisfaction: An Alternative Methodological Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 477-502, January.
    5. Yu-Shan Shih & Kuang-Hsun Liu, 2019. "Regression trees for detecting preference patterns from rank data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 683-702, September.
    6. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    7. Francesca Pagliara & Filomena Mauriello & Lucia Russo, 2020. "A Regression Tree Approach for Investigating the Impact of High Speed Rail on Tourists’ Choices," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    8. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    9. Eric Kamwa & Vincent Merlin, 2018. "The Likelihood of the Consistency of Collective Rankings under Preferences Aggregation with Four Alternatives using Scoring Rules: A General Formula and the Optimal Decision Rule," Working Papers hal-01757742, HAL.
    10. Yoo, Yeawon & Escobedo, Adolfo R. & Skolfield, J. Kyle, 2020. "A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1025-1041.

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