IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v43y2016i15p2866-2885.html
   My bibliography  Save this article

Generalized additive models with unknown link function including variable selection

Author

Listed:
  • Gerhard Tutz
  • Sebastian Petry

Abstract

The generalized additive model is a well established and strong tool that allows modelling smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, estimates can be biased. A procedure is proposed that simultaneously estimates the form of the link function and the unknown form of the predictor functions including selection of predictors. The procedure is based on boosting methodology, which obtains estimates by using a sequence of weak learners. It strongly dominates fitting procedures that are unable to modify a given link function if the true link function deviates from the fixed function. The performance of the procedure is shown in simulation studies and illustrated by real world examples.

Suggested Citation

  • Gerhard Tutz & Sebastian Petry, 2016. "Generalized additive models with unknown link function including variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2866-2885, November.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2866-2885
    DOI: 10.1080/02664763.2016.1155109
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1155109
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1155109?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.

    References listed on IDEAS

    as
    1. Xia Cui & Wolfgang Karl Härdle & Lixing Zhu, 2009. "Generalized single-index models: The EFM approach," SFB 649 Discussion Papers SFB649DP2009-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    Full references (including those not matched with items on IDEAS)

    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:taf:japsta:v:43:y:2016:i:15:p:2866-2885. 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: 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.