IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v39y2024i4d10.1007_s00180-023-01414-9.html
   My bibliography  Save this article

Rank estimation for the function-on-scalar model

Author

Listed:
  • Jun Sun

    (Anhui University of Finance and Economics)

  • Mingtao Zhao

    (Anhui University of Finance and Economics)

  • Ning Li

    (Hefei University)

  • Jing Yang

    (Hunan Normal University)

Abstract

Rank regression method has been widely pursued for robust inference in statistical models. Unfortunately, there does not exist related literature for the function-on-scalar model, which is the focus of this paper. We study the robust estimation based on rank regression and B-spline approximations for the function-on-scalar model and further establish the theoretical properties of the proposed method under regularity conditions. Extensive simulation studies and two real data applications are given to illustrate the merits of the proposed approach. Numerical results show that the proposed method is competitive with existing robust estimation procedures.

Suggested Citation

  • Jun Sun & Mingtao Zhao & Ning Li & Jing Yang, 2024. "Rank estimation for the function-on-scalar model," Computational Statistics, Springer, vol. 39(4), pages 1807-1823, June.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:4:d:10.1007_s00180-023-01414-9
    DOI: 10.1007/s00180-023-01414-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-023-01414-9
    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/s00180-023-01414-9?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:compst:v:39:y:2024:i:4:d:10.1007_s00180-023-01414-9. 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.