Unified Hybrid Censoring Samples from Power Pratibha Distribution and Its Applications

Hebatalla H. Mohammad, Khalaf S. Sultan and Mahmoud M. M. Mansour ()
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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

Mathematics, 2025, vol. 13, issue 14, 1-19

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.

Keywords: Power Pratibha Distribution; Unified Hybrid Censoring; Bayesian estimation; maximum likelihood estimation; reliability analysis; real data application (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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