IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i22p7797-7818.html
   My bibliography  Save this article

Complete consistency for the weighted least squares estimators in semiparametric regression models

Author

Listed:
  • Yutan Lv
  • Yunbao Yao
  • Jun Zhou
  • Xiaoqin Li
  • Ruiqi Yang
  • Xuejun Wang

Abstract

In this article, we consider the semiparametric regression model yi=xiβ+g(ti)+σiei, i=1,2,…,n, where σi2=f(ui), (xi,ti,ui) are known fixed design points, β is an unknown parameter to be estimated, g(·) and f(·) are unknown functions defined on a compact set. Assume that the random errors {ei, i≥1} are zero mean widely orthant dependent (WOD, for short) random variables. Under some suitable conditions, we investigate the complete consistency for the least squares estimators (LSE, for short) and the weighted least squares estimators (WLSE, for short) of β and g(·). In addition, a numerical simulation is given to study the numerical performance based on finite samples.

Suggested Citation

  • Yutan Lv & Yunbao Yao & Jun Zhou & Xiaoqin Li & Ruiqi Yang & Xuejun Wang, 2023. "Complete consistency for the weighted least squares estimators in semiparametric regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(22), pages 7797-7818, November.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:22:p:7797-7818
    DOI: 10.1080/03610926.2022.2050400
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2022.2050400
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2022.2050400?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.

    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:52:y:2023:i:22:p:7797-7818. 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.