IDEAS home Printed from https://ideas.repec.org/a/hin/jjmath/8874880.html
   My bibliography  Save this article

Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data

Author

Listed:
  • Fatimah Alshahrani
  • Ibrahim M. Almanjahie
  • Tawfik Benchikh
  • Omar Fetitah
  • Mohammed Kadi Attouch
  • Jun Fan

Abstract

This study investigates the estimation of the regression function using the kernel method in the presence of missing at random responses, assuming spatial dependence, and complete observation of the functional regressor. We construct the asymptotic properties of the established estimator and derive the probability convergence (with rates) as well as the asymptotic normality of the estimator under certain weak conditions. Simulation studies are then presented to examine and show the performance of our proposed estimator. This is followed by examining a real data set to illustrate the suggested estimator’s efficacy and demonstrate its superiority. The results show that the proposed estimator outperforms existing estimators as the number of missing at random data increases.

Suggested Citation

  • Fatimah Alshahrani & Ibrahim M. Almanjahie & Tawfik Benchikh & Omar Fetitah & Mohammed Kadi Attouch & Jun Fan, 2023. "Asymptotic Normality of Nonparametric Kernel Regression Estimation for Missing at Random Functional Spatial Data," Journal of Mathematics, Hindawi, vol. 2023, pages 1-20, September.
  • Handle: RePEc:hin:jjmath:8874880
    DOI: 10.1155/2023/8874880
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/jmath/2023/8874880.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/jmath/2023/8874880.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2023/8874880?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
    ---><---

    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:hin:jjmath:8874880. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.