IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v44y2017i6p968-977.html
   My bibliography  Save this article

S-estimator in partially linear regression models

Author

Listed:
  • Yunlu Jiang

Abstract

In this paper, a robust estimator is proposed for partially linear regression models. We first estimate the nonparametric component using the penalized regression spline, then we construct an estimator of parametric component by using robust S-estimator. We propose an iterative algorithm to solve the proposed optimization problem, and introduce a robust generalized cross-validation to select the penalized parameter. Simulation studies and a real data analysis illustrate that the our proposed method is robust against outliers in the dataset or errors with heavy tails.

Suggested Citation

  • Yunlu Jiang, 2017. "S-estimator in partially linear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(6), pages 968-977, April.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:968-977
    DOI: 10.1080/02664763.2016.1189523
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1189523
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1189523?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. Aifen Feng & Jingya Fan & Zhengfen Jin & Mengmeng Zhao & Xiaogai Chang, 2023. "Research Based on High-Dimensional Fused Lasso Partially Linear Model," Mathematics, MDPI, vol. 11(12), pages 1-15, June.

    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:japsta:v:44:y:2017:i:6:p:968-977. 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/CJAS20 .

    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.