IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v100y2013i2p525-530.html
   My bibliography  Save this article

Efficient estimation of the censored linear regression model

Author

Listed:
  • Yuanyuan Lin
  • Kani Chen

Abstract

In linear regression or accelerated failure time models, complications in efficient estimation arise from the multiple roots of the efficient score and density estimation. This paper proposes a one-step efficient estimation method based on a counting process martingale, which has several advantages: it avoids the multiple-root problem, the initial estimator is easily available and the variance estimator can be obtained by employing plug-in rules. A simple and effective data-driven bandwidth selector is provided. The proposed estimator is proved to be semiparametric efficient, with the same asymptotic variance as the efficient estimator when the error distribution is known up to a location shift. Numerical studies with supportive evidence are presented. The proposal is applied to the Colorado Plateau uranium miners data. Copyright 2013, Oxford University Press.

Suggested Citation

  • Yuanyuan Lin & Kani Chen, 2013. "Efficient estimation of the censored linear regression model," Biometrika, Biometrika Trust, vol. 100(2), pages 525-530.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:2:p:525-530
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/ass073
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Ying Sheng & Yifei Sun & Detian Deng & Chiung‐Yu Huang, 2020. "Censored linear regression in the presence or absence of auxiliary survival information," Biometrics, The International Biometric Society, vol. 76(3), pages 734-745, September.
    2. Yifei Sun & Kwun Chuen Gary Chan & Jing Qin, 2018. "Simple and fast overidentified rank estimation for right†censored length†biased data and backward recurrence time," Biometrics, The International Biometric Society, vol. 74(1), pages 77-85, March.
    3. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2017. "Semiparametric estimation of the accelerated failure time model with partly interval‐censored data," Biometrics, The International Biometric Society, vol. 73(4), pages 1161-1168, December.

    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:oup:biomet:v:100:y:2013:i:2:p:525-530. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.