IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v35y2008i1p158-168.html
   My bibliography  Save this article

Robust Inference in Conditionally Linear Nonlinear Regression Models

Author

Listed:
  • ROBERT L. PAIGE
  • P. HARSHINI FERNANDO

Abstract

. We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, say α, in conditionally linear nonlinear regression models. We derive closed‐form expressions for robust conditional, marginal, profile and modified profile likelihood functions for α under elliptically contoured data distributions. Next, we develop robust exact‐F confidence intervals for α and consider robust Fieller intervals for ratios of regression parameters in linear models. Several well‐known examples are considered and Monte Carlo simulation results are presented.

Suggested Citation

  • Robert L. Paige & P. Harshini Fernando, 2008. "Robust Inference in Conditionally Linear Nonlinear Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(1), pages 158-168, March.
    • Handle: RePEc:bla:scjsta:v:35:y:2008:i:1:p:158-168
    DOI: 10.1111/j.1467-9469.2007.00570.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9469.2007.00570.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9469.2007.00570.x?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
    ---><---

    References listed on IDEAS

    as
    1. Patrick Marsh, "undated". "Some Geometry for the Maximal Invariant in Linear Regression," Discussion Papers 04/07, Department of Economics, University of York.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert Paige & A. Trindade & R. Wickramasinghe, 2014. "Extensions of saddlepoint-based bootstrap inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 961-981, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:bla:scjsta:v:35:y:2008:i:1:p:158-168. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

      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.