IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v108y2024i3d10.1007_s10182-023-00490-y.html
   My bibliography  Save this article

Bayesian generalized additive model selection including a fast variational option

Author

Listed:
  • Virginia X. He

    (University of Technology Sydney)

  • Matt P. Wand

    (University of Technology Sydney)

Abstract

We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach allows for the effects of continuous predictors to be categorized as either zero, linear or non-linear. Employment of carefully tailored auxiliary variables results in Gibbsian Markov chain Monte Carlo schemes for practical implementation of the approach. In addition, mean field variational algorithms with closed form updates are obtained. Whilst not as accurate, this fast variational option enhances scalability to very large data sets. A package in the R language aids use in practice.

Suggested Citation

  • Virginia X. He & Matt P. Wand, 2024. "Bayesian generalized additive model selection including a fast variational option," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 639-668, September.
    • Handle: RePEc:spr:alstar:v:108:y:2024:i:3:d:10.1007_s10182-023-00490-y
    DOI: 10.1007/s10182-023-00490-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10182-023-00490-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10182-023-00490-y?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.

    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:spr:alstar:v:108:y:2024:i:3:d:10.1007_s10182-023-00490-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.