IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v65y2024i4d10.1007_s00362-023-01480-7.html
   My bibliography  Save this article

Robust optimal subsampling based on weighted asymmetric least squares

Author

Listed:
  • Min Ren

    (Qufu Normal University)

  • Shengli Zhao

    (Qufu Normal University)

  • Mingqiu Wang

    (Qufu Normal University)

  • Xinbei Zhu

    (Virginia Tech University)

Abstract

With the development of contemporary science, a large amount of generated data includes heterogeneity and outliers in the response and/or covariates. Furthermore, subsampling is an effective method to overcome the limitation of computational resources. However, when data include heterogeneity and outliers, incorrect subsampling probabilities may select inferior subdata, and statistic inference on this subdata may have a far inferior performance. Combining the asymmetric least squares and $$L_2$$ L 2 estimation, this paper proposes a double-robustness framework (DRF), which can simultaneously tackle the heterogeneity and outliers in the response and/or covariates. The Poisson subsampling is implemented based on the DRF for massive data, and a more robust probability will be derived to select the subdata. Under some regularity conditions, we establish the asymptotic properties of the subsampling estimator based on the DRF. Numerical studies and actual data demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Min Ren & Shengli Zhao & Mingqiu Wang & Xinbei Zhu, 2024. "Robust optimal subsampling based on weighted asymmetric least squares," Statistical Papers, Springer, vol. 65(4), pages 2221-2251, June.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01480-7
    DOI: 10.1007/s00362-023-01480-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-023-01480-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-023-01480-7?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.

    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:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01480-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.