IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v72y2014i2p231-245.html
   My bibliography  Save this article

Approximate Bayesian computation with modified log-likelihood ratios

Author

Listed:
  • Laura Ventura
  • Nancy Reid

Abstract

The aim of this contribution is to discuss approximate Bayesian computation based on the asymptotic theory of modified likelihood roots and log-likelihood ratios. Results on third-order approximations for univariate posterior distributions, also in the presence of nuisance parameters, are reviewed and the computation of asymptotic credible sets for a vector parameter of interest is illustrated. All these approximations are available at little additional computational cost over simple first-order approximations. Some illustrative examples are discussed, with particular attention to the use of matching priors. Copyright Sapienza Università di Roma 2014

Suggested Citation

  • Laura Ventura & Nancy Reid, 2014. "Approximate Bayesian computation with modified log-likelihood ratios," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 231-245, August.
  • Handle: RePEc:spr:metron:v:72:y:2014:i:2:p:231-245
    DOI: 10.1007/s40300-014-0041-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s40300-014-0041-4
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s40300-014-0041-4?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. Ventura, Laura & Ruli, Erlis & Racugno, Walter, 2013. "A note on approximate Bayesian credible sets based on modified loglikelihood ratios," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2467-2472.
    2. Ventura, Laura & Sartori, Nicola & Racugno, Walter, 2013. "Objective Bayesian higher-order asymptotics in models with nuisance parameters," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 90-96.
    3. Ventura, Laura & Cabras, Stefano & Racugno, Walter, 2009. "Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 768-774.
    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. Erlis Ruli & Laura Ventura, 2021. "Can Bayesian, confidence distribution and frequentist inference agree?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 359-373, March.

    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.
    1. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
    2. Toyoto Tanaka & Yoshihiro Hirose & Fumiyasu Komaki, 2020. "Second-order matching prior family parametrized by sample size and matching probability," Statistical Papers, Springer, vol. 61(4), pages 1701-1717, August.
    3. Ventura, Laura & Ruli, Erlis & Racugno, Walter, 2013. "A note on approximate Bayesian credible sets based on modified loglikelihood ratios," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2467-2472.
    4. Ventura, Laura & Sartori, Nicola & Racugno, Walter, 2013. "Objective Bayesian higher-order asymptotics in models with nuisance parameters," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 90-96.
    5. Erlis Ruli & Laura Ventura, 2021. "Can Bayesian, confidence distribution and frequentist inference agree?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 359-373, March.
    6. Ventura, Laura & Racugno, Walter, 2012. "On interval and point estimators based on a penalization of the modified profile likelihood," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1285-1289.
    7. Samadrita Bera & Nabakumar Jana, 2022. "On estimating common mean of several inverse Gaussian distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 115-139, January.
    8. Yongku Kim & Woo Dong Lee & Sang Gil Kang, 2020. "A matching prior based on the modified profile likelihood for the common mean in multiple log-normal distributions," Statistical Papers, Springer, vol. 61(2), pages 543-573, April.

    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:metron:v:72:y:2014:i:2:p:231-245. 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: 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.