IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v28y1996i1p23-31.html
   My bibliography  Save this article

Nonparametric estimation in heteroskedastic regression

Author

Listed:
  • Akritas, Michael G.

Abstract

We consider the problem of making inferences about the parameters in a heteroskedastic regression model using the ranks of weighted observations. The model assumes symmetric error distribution and a parametric model for the error variance. It is shown that there is no loss in asymptotic efficiency due to estimating the unknown weights. This extends the theory of rank estimation in the heteroskedastic linear model.

Suggested Citation

  • Akritas, Michael G., 1996. "Nonparametric estimation in heteroskedastic regression," Statistics & Probability Letters, Elsevier, vol. 28(1), pages 23-31, June.
    • Handle: RePEc:eee:stapro:v:28:y:1996:i:1:p:23-31
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(95)00077-1
    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.

    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:28:y:1996:i:1:p:23-31. 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: 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.