IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v190y2022ics0167715222001705.html
   My bibliography  Save this article

On a prior based on the Wasserstein information matrix

Author

Listed:
  • Li, W.
  • Rubio, F.J.

Abstract

We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties.

Suggested Citation

  • Li, W. & Rubio, F.J., 2022. "On a prior based on the Wasserstein information matrix," Statistics & Probability Letters, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:stapro:v:190:y:2022:i:c:s0167715222001705
    DOI: 10.1016/j.spl.2022.109645
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715222001705
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2022.109645?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. Holger Dette & Christophe Ley & Francisco Rubio, 2018. "Natural (Non†)Informative Priors for Skew†symmetric Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(2), pages 405-420, June.
    2. Rubio, Francisco Javier & Liseo, Brunero, 2014. "On the independence Jeffreys prior for skew-symmetric models," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 91-97.
    3. Shun-ichi Amari & Takeru Matsuda, 2022. "Wasserstein statistics in one-dimensional location scale models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 33-47, February.
    4. Shun‐ichi Amari, 2021. "Information Geometry," International Statistical Review, International Statistical Institute, vol. 89(2), pages 250-273, August.
    5. repec:dau:papers:123456789/3222 is not listed on IDEAS
    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. Mattsson, Lars-Göran & Weibull, Jörgen W., 2023. "An analytically solvable principal-agent model," Games and Economic Behavior, Elsevier, vol. 140(C), pages 33-49.
    2. Ghaderinezhad, Fatemeh & Ley, Christophe & Serrien, Ben, 2022. "The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

    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:stapro:v:190:y:2022:i:c:s0167715222001705. 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.