IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v66y2025i1d10.1007_s00362-024-01658-7.html
   My bibliography  Save this article

Nonparametric Bayesian inferences on the skewed data using a Dirichlet process mixture model

Author

Listed:
  • Amin Ghalamfarsa Mostofi

    (Shiraz University)

  • Mahmood Kharrati-Kopaei

    (Shiraz University)

Abstract

This paper presents a new mixture model that can be regarded as a modified version of the Dirichlet process normal mixture models. In this model, the component distribution depends on a parameter whose value affects directly the skewness of the population distribution. Unlike the usual normal mixture model, one can impose prior information on the skewness parameter and make inferences. A nonparametric Bayesian approach is proposed to make inferences about the parameters of the model, including mean, variance, mode, and skewness parameters. An example is given to illustrate the use of the proposed mixture model in testing symmetry and fitting a distribution to data. We also compare our proposed method with two existing methods in terms of mean squared error and mean integrated squared error of the predictive density estimation.

Suggested Citation

  • Amin Ghalamfarsa Mostofi & Mahmood Kharrati-Kopaei, 2025. "Nonparametric Bayesian inferences on the skewed data using a Dirichlet process mixture model," Statistical Papers, Springer, vol. 66(1), pages 1-24, February.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01658-7
    DOI: 10.1007/s00362-024-01658-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-024-01658-7
    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/s00362-024-01658-7?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:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01658-7. 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.