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Median constrained bucket order rank aggregation

Author

Listed:
  • Antonio D’Ambrosio

    (University of Naples Federico II)

  • Carmela Iorio

    (University of Naples Federico II)

  • Michele Staiano

    (University of Naples Federico II)

  • Roberta Siciliano

    (University of Naples Federico II)

Abstract

The rank aggregation problem can be summarized as the problem of aggregating individual preferences expressed by a set of judges to obtain a ranking that represents the best synthesis of their choices. Several approaches for handling this problem have been proposed and are generally linked with either axiomatic frameworks or alternative strategies. In this paper, we present a new definition of median ranking and frame it within the Kemeny’s axiomatic framework. Moreover, we show the usefulness of our approach in a practical case about triage prioritization.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-0858-z
    DOI: 10.1007/s00180-018-0858-z
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    References listed on IDEAS

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    Cited by:

    1. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    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. Akbari, Sina & Escobedo, Adolfo R., 2023. "Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties," Omega, Elsevier, vol. 119(C).
    4. Arcagni, Alberto & Avellone, Alessandro & Fattore, Marco, 2022. "Complexity reduction and approximation of multidomain systems of partially ordered data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).

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