IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v45y2016i9p2665-2678.html
   My bibliography  Save this article

Analysis of ordered probit model with surrogate response data and measurement error in covariates

Author

Listed:
  • Surupa Roy

Abstract

Often in data arising out of epidemiologic studies, covariates are subject to measurement error. In addition ordinal responses may be misclassified into a category that does not reflect the true state of the respondents. The goal of the present work is to develop an ordered probit model that corrects for the classification errors in ordinal responses and/or measurement error in covariates. Maximum likelihood method of estimation is used. Simulation study reveals the effect of ignoring measurement error and/or classification errors on the estimates of the regression coefficients. The methodology developed is illustrated through a numerical example.

Suggested Citation

  • Surupa Roy, 2016. "Analysis of ordered probit model with surrogate response data and measurement error in covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(9), pages 2665-2678, May.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:9:p:2665-2678
    DOI: 10.1080/03610926.2014.887115
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.887115
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2014.887115?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:45:y:2016:i:9:p:2665-2678. 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.