IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i6d10.1007_s00180-022-01296-3.html
   My bibliography  Save this article

An extended approach for the generalized powered uniform distribution

Author

Listed:
  • Carlos Rondero-Guerrero

    (Autonomous University of Hidalgo State)

  • Isidro González-Hernández

    (Autonomous University of Hidalgo State)

  • Carlos Soto-Campos

    (Autonomous University of Hidalgo State)

Abstract

A new uniform distribution model, generalized powered uniform distribution (GPUD), which is based on incorporating the parameter k into the probability density function (pdf) associated with the power of random variable values and includes a powered mean operator, is introduced in this paper. From this new model, the shape properties of the pdf as well as the higher-order moments, the moment generating function, the model that simulates the GPUD and other important statistics can be derived. This approach allows the generalization of the distribution presented by Jayakumar and Sankaran (2016) through the new $${ GPUD }_{ (J-S)}$$ GPUD ( J - S ) distribution. Two sets of real data related to COVID-19 and bladder cancer were tested to demonstrate the proposed model’s potential. The maximum likelihood method was used to calculate the parameter estimators by applying the maxLik package in R. The results showed that this new model is more flexible and useful than other comparable models.

Suggested Citation

  • Carlos Rondero-Guerrero & Isidro González-Hernández & Carlos Soto-Campos, 2025. "An extended approach for the generalized powered uniform distribution," Computational Statistics, Springer, vol. 40(6), pages 2907-2930, July.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:6:d:10.1007_s00180-022-01296-3
    DOI: 10.1007/s00180-022-01296-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-022-01296-3
    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/s00180-022-01296-3?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:compst:v:40:y:2025:i:6:d:10.1007_s00180-022-01296-3. 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.