IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i14p2220-d1696973.html
   My bibliography  Save this article

Unified Hybrid Censoring Samples from Power Pratibha Distribution and Its Applications

Author

Listed:
  • Hebatalla H. Mohammad

    (Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Khalaf S. Sultan

    (Mathematics Department, Faculty of Science, Al-Azhar University, Nasr City, Cairo 11884, Egypt)

  • Mahmoud M. M. Mansour

    (Department of Basic Science, Faculty of Engineering, The British University in Egypt, El Sherook City, Cairo P.O. Box 43, Egypt)

Abstract

This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, especially when the dataset is not very large and when a lot of data is missing. Real-world tests of electromigration failure data and banking service times help to test the methods. In both situations, the PPD shows it can be used successfully in different reliability settings. By joining advanced censoring models and reliable statistical methods, this research gives a helpful toolset to experts in reliability analysis and statistics.

Suggested Citation

  • Hebatalla H. Mohammad & Khalaf S. Sultan & Mahmoud M. M. Mansour, 2025. "Unified Hybrid Censoring Samples from Power Pratibha Distribution and Its Applications," Mathematics, MDPI, vol. 13(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:14:p:2220-:d:1696973
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/14/2220/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/14/2220/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:13:y:2025:i:14:p:2220-:d:1696973. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.