IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v107y2012i498p661-672.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2012.682539
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2012.682539?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Schimek Michael G. & Švendová Vendula & Budinská Eva & Kugler Karl G. & 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.
    9. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:107:y:2012:i:498:p:661-672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.