IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i8d10.1007_s00180-025-01638-x.html
   My bibliography  Save this article

Enhancing parameter estimation in finite mixture of generalized normal distributions

Author

Listed:
  • Pierdomenico Duttilo

    (University of Padova)

  • Stefano Antonio Gattone

    (University “G. d’Annunzio” of Chieti-Pescara)

Abstract

Mixtures of generalized normal distributions (MGND) have gained popularity for modelling datasets with complex statistical behaviours. However, the estimation of the shape parameter within the maximum likelihood framework is quite complex, presenting the risk of numerical and degeneracy issues. This study introduced an expectation conditional maximization algorithm that includes an adaptive step size function within Newton–Raphson updates of the shape parameter and a modified criterion for stopping the EM iterations. Through extensive simulations, the effectiveness of the proposed algorithm in overcoming the limitations of existing approaches, especially in scenarios with high shape parameter values, high parameters overalp and low sample sizes, is shown. A detailed comparative analysis with a mixture of normals and Student-t distributions revealed that the MGND model exhibited superior goodness-of-fit performance when used to fit the density of the returns of 50 stocks belonging to the Euro Stoxx index.

Suggested Citation

  • Pierdomenico Duttilo & Stefano Antonio Gattone, 2025. "Enhancing parameter estimation in finite mixture of generalized normal distributions," Computational Statistics, Springer, vol. 40(8), pages 4607-4634, November.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01638-x
    DOI: 10.1007/s00180-025-01638-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-025-01638-x
    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/s00180-025-01638-x?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01638-x. 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.