IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v56y2002i3p239-250.html
   My bibliography  Save this article

A unified treatment of direct and indirect estimation of a probability density and its derivatives

Author

Listed:
  • Abdous, Belkacem
  • Germain, Stéphane
  • Ghazzali, Nadia

Abstract

This paper presents convolution-based estimates of a probability density and its derivatives. The proposed estimates can handle either contaminated data or not and they comprehend some classical estimates such that kernel, regularization estimates. By putting these direct and indirect estimation problems in the same framework, we clearly see how the estimates performances are affected by contamination and by the order of the derivative to be estimated. Minimax optimal rates for the MISE criterion are proposed.

Suggested Citation

  • Abdous, Belkacem & Germain, Stéphane & Ghazzali, Nadia, 2002. "A unified treatment of direct and indirect estimation of a probability density and its derivatives," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 239-250, February.
    • Handle: RePEc:eee:stapro:v:56:y:2002:i:3:p:239-250
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(01)00164-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ja‐Yong Koo, 1999. "Logspline Deconvolution in Besov Space," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 73-86, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Salim Bouzebda & Mohamed Chaouch & Sultana Didi Biha, 2022. "Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 737-771, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jérémie Bigot & Sébastien Van Bellegem, 2009. "Log‐density Deconvolution by Wavelet Thresholding," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 749-763, December.

    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:eee:stapro:v:56:y:2002:i:3:p:239-250. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.