[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v20y2024i1p245-278n1006.html
   My bibliography  Save this article

Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset

Author

Listed:
  • Aydın Dursun

    (Department of Statistics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Türkiye)

  • Yılmaz Ersin

    (Department of Statistics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Türkiye)

  • Chamidah Nur

    (Department of Mathematics, Faculty of Science and Technology, Airlangaa University, Surabaya, Indonesia)

  • Lestari Budi

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Jember, Jember, Indonesia)

Abstract
This paper considers a partially linear regression model relating a right-censored response variable to predictors and an extra covariate with measured error. The main problem here is that censorship and measurement error problems need to be solved to estimate the model correctly. In this sense, we propose three modified semiparametric estimators obtained from local polynomial regression, kernel smoothing, and B-spline smoothing methods based on kernel deconvolution approach and synthetic data transformation. Here, kernel deconvolution technique is used to solve the measurement error problem in the model and synthetic data transformation is considered to add the effect of censorship to the estimation procedure, which is a very common method in the literature. The performances of the introduced estimators are compared in the detailed Monte-Carlo simulation study. In addition, Carotid endarterectomy data is used as real-world data example and results are presented. According to the results, it is seen that the deconvoluted local polynomial method gives more qualified estimates than other two methods.

Suggested Citation

  • Aydın Dursun & Yılmaz Ersin & Chamidah Nur & Lestari Budi, 2024. "Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset," The International Journal of Biostatistics, De Gruyter, vol. 20(1), pages 245-278.
  • Handle: RePEc:bpj:ijbist:v:20:y:2024:i:1:p:245-278:n:1006
    DOI: 10.1515/ijb-2022-0044
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2022-0044
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/ijb-2022-0044?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.

    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:bpj:ijbist:v:20:y:2024:i:1:p:245-278:n:1006. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.