IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i7p3438-3458.html
   My bibliography  Save this article

A weighted least-squares cross-validation bandwidth selector for kernel density estimation

Author

Listed:
  • C. Tenreiro

Abstract

Since the late 1980s, several methods have been considered in the literature to reduce the sample variability of the least-squares cross-validation bandwidth selector for kernel density estimation. In this article, a weighted version of this classical method is proposed and its asymptotic and finite-sample behavior is studied. The simulation results attest that the weighted cross-validation bandwidth performs quite well, presenting a better finite-sample performance than the standard cross-validation method for “easy-to-estimate” densities, and retaining the good finite-sample performance of the standard cross-validation method for “hard-to-estimate” ones.

Suggested Citation

  • C. Tenreiro, 2017. "A weighted least-squares cross-validation bandwidth selector for kernel density estimation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(7), pages 3438-3458, April.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:7:p:3438-3458
    DOI: 10.1080/03610926.2015.1062108
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2015.1062108
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2015.1062108?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carlos Tenreiro, 2022. "On automatic kernel density estimate-based tests for goodness-of-fit," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 717-748, September.
    2. El Heda, Khadijetou & Louani, Djamal, 2018. "Optimal bandwidth selection in kernel density estimation for continuous time dependent processes," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 9-19.
    3. Olga Y. Savchuk, 2020. "One-sided cross-validation for nonsmooth density functions," Computational Statistics, Springer, vol. 35(3), pages 1253-1272, September.

    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:46:y:2017:i:7:p:3438-3458. 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.