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An Extended Mallows Model for Ranked Data Aggregation

Author

Listed:
  • Han Li
  • Minxuan Xu
  • Jun S. Liu
  • Xiaodan Fan

Abstract

In this article, we study the rank aggregation problem, which aims to find a consensus ranking by aggregating multiple ranking lists. To address the problem probabilistically, we formulate an elaborate ranking model for full and partial rankings by generalizing the Mallows model. Our model assumes that the ranked data are generated through a multistage ranking process that is explicitly governed by parameters that measure the overall quality and stability of the process. The new model is quite flexible and has a closed form expression. Under mild conditions, we can derive a few useful theoretical properties of the model. Furthermore, we propose an efficient statistic called rank coefficient to detect over-correlated rankings and a hierarchical ranking model to fit the data. Through extensive simulation studies and real applications, we evaluate the merits of our models and demonstrate that they outperform the state-of-the-art methods in diverse scenarios. Supplementary materials for this article are available online.

Suggested Citation

  • Han Li & Minxuan Xu & Jun S. Liu & Xiaodan Fan, 2020. "An Extended Mallows Model for Ranked Data Aggregation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 730-746, April.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:530:p:730-746
    DOI: 10.1080/01621459.2019.1573733
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    Cited by:

    1. Kateri, Maria & Nikolov, Nikolay I., 2022. "A generalized Mallows model based on ϕ-divergence measures," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Maryam Aghayerashti & Ehsan Bahrami Samani & Mojtaba Ganjali, 2023. "Bayesian Latent Variable Model of Mixed Correlated Rank and Beta-Binomial Responses with Missing Data for the International Statistical Literacy Project Poster Competition," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 210-250, May.
    3. (Corresponding author) Gavin Yamey & Kaci Kennedy McDade & Wenhui Mao & Ekene Osakwe, 2022. "Financing Research And Development For New Vaccines In Developing Asia-Pacific Countries," Asia-Pacific Sustainable Development Journal, United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), vol. 29(2), pages 125-153, November.

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