IDEAS home Printed from https://ideas.repec.org/a/taf/tstfxx/v3y2019i1p2-13.html
   My bibliography  Save this article

Prior-based Bayesian information criterion

Author

Listed:
  • M. J. Bayarri
  • James O. Berger
  • Woncheol Jang
  • Surajit Ray
  • Luis R. Pericchi
  • Ingmar Visser

Abstract

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one overall sample size n). We also consider a modification of PBIC which is more favourable to complex models.

Suggested Citation

  • M. J. Bayarri & James O. Berger & Woncheol Jang & Surajit Ray & Luis R. Pericchi & Ingmar Visser, 2019. "Prior-based Bayesian information criterion," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(1), pages 2-13, January.
    • Handle: RePEc:taf:tstfxx:v:3:y:2019:i:1:p:2-13
    DOI: 10.1080/24754269.2019.1582126
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24754269.2019.1582126
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24754269.2019.1582126?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. Su, Zheren & Wang, Yaping & Zhou, Yingchun, 2020. "On maximin distance and nearly orthogonal Latin hypercube designs," Statistics & Probability Letters, Elsevier, vol. 166(C).
    2. D. Vélez & M. E. Pérez & L. R. Pericchi, 2022. "Increasing the replicability for linear models via adaptive significance levels," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 771-789, September.

    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:tstfxx:v:3:y:2019:i:1:p:2-13. 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/tstf .

    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.