IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v27y2000i4p495-507.html
   My bibliography  Save this article

An algorithm for generalized monotonic smoothing

Author

Listed:
  • Zheng Wang

Abstract

In this paper, an algorithm for Generalized Monotonic Smoothing (GMS) is developed as an extension to exponential family models of the monotonic smoothing techniques proposed by Ramsay (1988, 1998a,b). A two-step algorithm is used to estimate the coefficients of bases and the linear term. We show that the algorithm can be embedded into the iterative re-weighted least square algorithm that is typically used to estimate the coefficients in Generalized Linear Models. Thus, the GMS estimator can be computed using existing routines in S-plus and other statistical software. We apply the GMS model to the Down's syndrome data set and compare the results with those from Generalized Additive Model estimation. The choice of smoothing parameter and testing of monotonicity are also discussed.

Suggested Citation

  • Zheng Wang, 2000. "An algorithm for generalized monotonic smoothing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(4), pages 495-507.
    • Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:495-507
    DOI: 10.1080/02664760050003678
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003678
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760050003678?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.

    Citations

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


    Cited by:

    1. Wei Wang & Dylan S. Small, 2015. "Monotone B-Spline Smoothing for a Generalized Linear Model Response," The American Statistician, Taylor & Francis Journals, vol. 69(1), pages 28-33, February.

    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:japsta:v:27:y:2000:i:4:p:495-507. 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/CJAS20 .

    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.