IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i15p5213-5245.html
   My bibliography  Save this article

Two-time-scale nonparametric recursive regression estimator for independent functional data

Author

Listed:
  • Yousri Slaoui

Abstract

In this paper, we propose and investigate a new kernel regression estimators based on the two-time-scale stochastic approximation algorithm in the case of independent functional data. We study the properties of the proposed recursive estimators and compare them with the recursive estimators based on single-time-scale stochastic algorithm proposed by Slaoui and to the non-recursive estimator proposed by Slaoui. It turns out that, with an adequate choice of the parameters, the proposed two-time-scale estimators perform better than the recursive estimators constructed using single-time-scale stochastic algorithm. We corroborate these theoretical results through some simulations and two real datasets.

Suggested Citation

  • Yousri Slaoui, 2023. "Two-time-scale nonparametric recursive regression estimator for independent functional data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(15), pages 5213-5245, August.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:15:p:5213-5245
    DOI: 10.1080/03610926.2021.2004428
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.2004428
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.2004428?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.

    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:lstaxx:v:52:y:2023:i:15:p:5213-5245. 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/lsta .

    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.