IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i1p68-81.html
   My bibliography  Save this article

Estimation of the additive hazards model with linear inequality restrictions based on current status data

Author

Listed:
  • Yanqin Feng
  • Jianguo Sun
  • Lingli Sun

Abstract

The additive hazards model is one of the commonly used models for failure time data analysis and many authors have discussed its estimation under various situations. In this paper, we consider the same problem but under some inequality constraints when one faces current status data, for which it does not seem to exist an established estimation procedure due to the difficulties involved. In particular, the restricted maximum likelihood estimation approach is derived and the asymptotic properties of the resulting estimator of regression parameters are established with the use of some optimization methods. Simulation studies are conducted to assess the finite-sample performance of the proposed method and suggest that it works well for practical situations. A real data application is provided to demonstrate the utility of the proposed approach.

Suggested Citation

  • Yanqin Feng & Jianguo Sun & Lingli Sun, 2022. "Estimation of the additive hazards model with linear inequality restrictions based on current status data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(1), pages 68-81, January.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:1:p:68-81
    DOI: 10.1080/03610926.2020.1742922
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1742922
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1742922?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:51:y:2022:i:1:p:68-81. 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.