IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v49y2020i23p5666-5685.html
   My bibliography  Save this article

Spatial local linear estimation of the L1-conditional quantiles for functional regressors

Author

Listed:
  • Ibrahim M. Almanjahie
  • Zouaoui Chikr Elmezouar
  • Bachir Ahmed Bachir
  • Zoulikha Kaid

Abstract

L1-norm approach is used to construct the local linear estimator of the spatial regression quantile for functional regressors. Under mixing spatial condition, we establish the almost complete convergence of the constructed approach. The applicability of the constructed estimator is examined by a Monte-Carlo study. The finite sample performance of the proposed estimator is compared to the classical kernel estimator of the functional spatial quantile regression. The result indicates that our new approach is more accurate than the classical one.

Suggested Citation

  • Ibrahim M. Almanjahie & Zouaoui Chikr Elmezouar & Bachir Ahmed Bachir & Zoulikha Kaid, 2020. "Spatial local linear estimation of the L1-conditional quantiles for functional regressors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(23), pages 5666-5685, December.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:23:p:5666-5685
    DOI: 10.1080/03610926.2019.1620781
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1620781
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1620781?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:49:y:2020:i:23:p:5666-5685. 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.