IDEAS home Printed from https://ideas.repec.org/a/cup/astinb/v56y2026i2p588-610_12.html

Enriched truncated exponentiated generalized family of distributions with application to heavy-tailed data

Author

Listed:
  • Dankwa, Richard
  • Cooray, Kahadawala

Abstract

A novel family of statistical distributions, called enriched truncated exponentiated generalized family, is theoretically developed to model heavy-tailed data. One of the three-parameter sub-models of this family derived from log-logistic distribution is comprehensively studied. The statistical properties are explored, including moments and Fisher information matrix. In addition, tail-heaviness is studied using the tail-index approach. The method of maximum likelihood is used for parameter estimation, and existence and uniqueness of these estimators are shown. The flexibility of the new family is further validated by applying to the Norwegian fire insurance claim dataset. The goodness-of-fit measures are used to illustrate the adequacy of the proposed family of distributions. Furthermore, a backtesting procedure is conducted for well-known risk measures to assess the accuracy of the right tail fit.

Suggested Citation

  • Dankwa, Richard & Cooray, Kahadawala, 2026. "Enriched truncated exponentiated generalized family of distributions with application to heavy-tailed data," ASTIN Bulletin, Cambridge University Press, vol. 56(2), pages 588-610, May.
    • Handle: RePEc:cup:astinb:v:56:y:2026:i:2:p:588-610_12
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0515036125100810/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    More about this item

    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:cup:astinb:v:56:y:2026:i:2:p:588-610_12. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/asb .

    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.