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Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists

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  • Peter Hall
  • Michael G. Schimek

Abstract

Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We can aggregate the rank lists by assigning one if the two assessors agree, and zero otherwise, and we can modify this approach to make it robust against irregularities. In this article, we consider methods and algorithms that can be used to address this problem. We study their theoretical properties in the case of a model based on nonstationary Bernoulli trials, and we report on their numerical properties for both simulated and real data.

Suggested Citation

  • Peter Hall & Michael G. Schimek, 2012. "Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 661-672, June.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:661-672
    DOI: 10.1080/01621459.2012.682539
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    Citations

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    Cited by:

    1. Schimek Michael G. & Budinská Eva & Kugler Karl G. & Švendová Vendula & Ding Jie & Lin Shili, 2015. "TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 311-316, June.
    2. Donald Margaret R. & Wilson Susan R., 2017. "Comparison and visualisation of agreement for paired lists of rankings," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 31-45, March.
    3. Li, Yumeng & Wang, Ran & Yao, Nian & Zhang, Shuguang, 2017. "A moderate deviation principle for stochastic Volterra equation," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 79-85.
    4. 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.
    5. Giuseppe Jurman & Samantha Riccadonna & Roberto Visintainer & Cesare Furlanello, 2012. "Algebraic Comparison of Partial Lists in Bioinformatics," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-20, May.
    6. Arboretti, Rosa & Bonnini, Stefano & Corain, Livio & Salmaso, Luigi, 2014. "A permutation approach for ranking of multivariate populations," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 39-57.
    7. Švendová, Vendula & Schimek, Michael G., 2017. "A novel method for estimating the common signals for consensus across multiple ranked lists," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 122-135.
    8. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    9. 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.

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