IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v31y2025i1p1-12n1001.html
   My bibliography  Save this article

Adaptive Bayesian bandwidth based on LPE kernel for asymmetric kernel density

Author

Listed:
  • Makhloufi Sylia

    (Department of Computer Science, Faculty of Exact Sciences; and Research Unit LaMOS, 210392 University of Bejaia , Bejaia, Algeria)

  • Zougab Nabil

    (Department of Electrical Engineering, Faculty of Technology; and Research Unit LaMOS, 210392 University of Bejaia , Bejaia, Algeria)

  • Ziane Yasmina

    (Department of Operational Research, Faculty of Exact Sciences; and Research Unit LaMOS, 210392 University of Bejaia , Bejaia, Algeria)

  • Adjabi Smail

    (Department of Operational Research, Faculty of Exact Sciences; and Research Unit LaMOS, 210392 University of Bejaia , Bejaia, Algeria)

Abstract

In this work, we propose to estimate the probability density function for nonnegative support using an LPE kernel with the adaptive Bayesian approach for bandwidth selection. For this, we exploit the conjugality between the LPE kernel and a prior distribution to develop the explicit form of the variable bandwidth parameter. Simulation studies are realized to evaluate the performance of the proposed adaptive Bayesian LPE kernel approach, with the classical Plug-In and UCV methods. On the other hand, we have carried out a comparative study between the proposed approach and two other adaptive Bayesian approaches with BSPE and gamma kernels. Moreover, real data sets are presented to illustrate the findings.

Suggested Citation

  • Makhloufi Sylia & Zougab Nabil & Ziane Yasmina & Adjabi Smail, 2025. "Adaptive Bayesian bandwidth based on LPE kernel for asymmetric kernel density," Monte Carlo Methods and Applications, De Gruyter, vol. 31(1), pages 1-12.
    • Handle: RePEc:bpj:mcmeap:v:31:y:2025:i:1:p:1-12:n:1001
    DOI: 10.1515/mcma-2024-2021
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2024-2021
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2024-2021?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:bpj:mcmeap:v:31:y:2025:i:1:p:1-12:n:1001. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.