IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v33y1984i1p120-122.html
   My bibliography  Save this article

A Remark on Algorithm as 176. Kernel Density Estimation Using the Fast Fourier Transform

Author

Listed:
  • M. C. Jones
  • H. W. Lotwick

Abstract

No abstract is available for this item.

Suggested Citation

  • M. C. Jones & H. W. Lotwick, 1984. "A Remark on Algorithm as 176. Kernel Density Estimation Using the Fast Fourier Transform," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 120-122, March.
    • Handle: RePEc:bla:jorssc:v:33:y:1984:i:1:p:120-122
    DOI: 10.2307/2347674
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347674
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347674?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
    ---><---

    Citations

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


    Cited by:

    1. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    2. Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
    3. Berwin A. TURLACH, "undated". "Bandwidth selection in kernel density estimation: a rewiew," Statistic und Oekonometrie 9307, Humboldt Universitaet Berlin.
    4. Pasha Andreyanov & Grigory Franguridi, 2021. "Nonparametric inference on counterfactuals in first-price auctions," Papers 2106.13856, arXiv.org, revised Oct 2024.
    5. Said Benlakhdar & Mohammed Rziza & Rachid Oulad Haj Thami, 2022. "Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energy," Computational Statistics, Springer, vol. 37(4), pages 1599-1619, 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:bla:jorssc:v:33:y:1984:i:1:p:120-122. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.