IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v52y2023i6p1744-1766.html
   My bibliography  Save this article

Jackknife empirical likelihood of error variance for partially linear varying-coefficient model with missing covariates

Author

Listed:
  • Yuye Zou
  • Chengxin Wu
  • Guoliang Fan
  • Riquan Zhang

Abstract

In this paper, we apply the profile least-square method and inverse probability weighted method to define estimation of the error variance in partially linear varying-coefficient model when the covariates are missing at random. At the same time, we construct a jackknife estimator and jackknife empirical likelihood (JEL) statistic of the error variance, respectively. It is proved that the proposed estimators are asymptotical normality and the JEL statistic admits a limiting standard chi-square distribution. A simulation study is conducted to compare the JEL method with the normal approximation approach in terms of coverage probabilities and average interval lengths, and a comparison of the proposed estimators is done based on sample means, biases and mean square errors under different settings. Subsequently, a real data set is analyzed for illustration of the proposed methods.

Suggested Citation

  • Yuye Zou & Chengxin Wu & Guoliang Fan & Riquan Zhang, 2023. "Jackknife empirical likelihood of error variance for partially linear varying-coefficient model with missing covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(6), pages 1744-1766, March.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:6:p:1744-1766
    DOI: 10.1080/03610926.2021.1938128
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1938128
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1938128?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:6:p:1744-1766. 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.