IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v12y2025i2d10.1007_s40745-024-00527-2.html
   My bibliography  Save this article

Half Logistic Generalized Rayleigh Distribution for Modeling Hydrological Data

Author

Listed:
  • Adebisi A. Ogunde

    (University of Ibadan)

  • Subhankar Dutta

    (Vellore Institute of Technology, Chennai Campus)

  • Ehab M. Almetawally

    (Delta University for Science and Technology)

Abstract

This article introduced a three-parameter extension of the Generalized Rayleigh distribution called half-logistic Generalized Rayleigh distribution, which has submodels the Generalized Rayleigh and Rayleigh distribution. The proposed model is quite flexible and adaptable to model any kind of life-time data. Its probability density function may sometimes be unimodal and its corresponding hazard rate may be of monotone or non-monotone shape. Standard statistical properties such as it ordinary and incomplete moments, quantile function, moment generating function, reliability function, stochastic ordering, order statistics, Renyi, and $${\varvec{\delta}}$$ δ -entropy are obtained. The maximum likelihood method is used to obtain the estimates of the model parameters. Two practical examples of hydrological data sets are presented.

Suggested Citation

  • Adebisi A. Ogunde & Subhankar Dutta & Ehab M. Almetawally, 2025. "Half Logistic Generalized Rayleigh Distribution for Modeling Hydrological Data," Annals of Data Science, Springer, vol. 12(2), pages 667-694, April.
  • Handle: RePEc:spr:aodasc:v:12:y:2025:i:2:d:10.1007_s40745-024-00527-2
    DOI: 10.1007/s40745-024-00527-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-024-00527-2
    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/s40745-024-00527-2?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:aodasc:v:12:y:2025:i:2:d:10.1007_s40745-024-00527-2. 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.