IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v131y2014icp126-148.html
   My bibliography  Save this article

Asymptotic expansion of the posterior density in high dimensional generalized linear models

Author

Listed:
  • Dasgupta, Shibasish
  • Khare, Kshitij
  • Ghosh, Malay

Abstract

While developing a prior distribution for any Bayesian analysis, it is important to check whether the corresponding posterior distribution becomes degenerate in the limit to the true parameter value as the sample size increases. In the same vein, it is also important to understand a more detailed asymptotic behavior of posterior distributions. This is particularly relevant in the development of many nonsubjective priors. The present paper focuses on asymptotic expansions of posteriors for generalized linear models with canonical link functions when the number of regressors grows to infinity at a certain rate relative to the growth of the sample size. These expansions are then used to derive moment matching priors in the generalized linear model setting.

Suggested Citation

  • Dasgupta, Shibasish & Khare, Kshitij & Ghosh, Malay, 2014. "Asymptotic expansion of the posterior density in high dimensional generalized linear models," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 126-148.
  • Handle: RePEc:eee:jmvana:v:131:y:2014:i:c:p:126-148
    DOI: 10.1016/j.jmva.2014.06.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X14001432
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmva.2014.06.013?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. Ghosal, Subhashis, 2000. "Asymptotic Normality of Posterior Distributions for Exponential Families when the Number of Parameters Tends to Infinity," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 49-68, July.
    2. Martin Crowder, 1988. "Asymptotic expansions of posterior expectations, distributions and densities for stochastic processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 40(2), pages 297-309, June.
    3. Subhashis Ghosal & Tapas Samanta, 1997. "Asymptotic Expansions of Posterior Distributions in Nonregular Cases," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(1), pages 181-197, March.
    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.
    1. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    2. Alexandre Belloni & Victor Chernozhukov, 2014. "Posterior inference in curved exponential families under increasing dimensions," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 75-100, June.
    3. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    4. Kim, Jae-Young, 2012. "Model selection in the presence of nonstationarity," Journal of Econometrics, Elsevier, vol. 169(2), pages 247-257.
    5. Jing Zhou & Anirban Bhattacharya & Amy H. Herring & David B. Dunson, 2015. "Bayesian Factorizations of Big Sparse Tensors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1562-1576, December.
    6. Banerjee, Sayantan & Ghosal, Subhashis, 2015. "Bayesian structure learning in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 147-162.
    7. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    8. Hashimoto, Shintaro, 2021. "Predictive probability matching priors for a certain non-regular model," Statistics & Probability Letters, Elsevier, vol. 174(C).
    9. Jin, Xin & Bhattacharya, Anirban & Ghosh, Riddhi Pratim, 2024. "High-dimensional Bernstein–von Mises theorem for the Diaconis–Ylvisaker prior," Journal of Multivariate Analysis, Elsevier, vol. 200(C).
    10. Malay Ghosh & Victor Mergel & Ruitao Liu, 2011. "A general divergence criterion for prior selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 43-58, February.
    11. Kim, Jae-Young, 2014. "An alternative quasi likelihood approach, Bayesian analysis and data-based inference for model specification," Journal of Econometrics, Elsevier, vol. 178(P1), pages 132-145.
    12. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.

    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:eee:jmvana:v:131:y:2014:i:c:p:126-148. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.