Analysis of the Stress–Strength Model Using Uniform Truncated Negative Binomial Distribution under Progressive Type-II Censoring

Author:

EL-Sagheer Rashad M.12ORCID,Eliwa Mohamed S.34ORCID,El-Morshedy Mahmoud56ORCID,Al-Essa Laila A.7,Al-Bossly Afrah5,Abd-El-Monem Amel8

Affiliation:

1. Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Egypt

2. High Institute of Computer and Management Information System, First Statement, New Cairo 11865, Egypt

3. Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia

4. Department of Statistics and Computer Science, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

5. Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

6. Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

7. Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

8. Department of Mathematics, Faculty of Education, Ain-Shams University, Cairo 11341, Egypt

Abstract

In this study, we introduce a novel estimation technique for assessing the reliability parameter R=P(Y<X) of the uniform truncated negative binomial distribution (UTNBD) in the context of stress–strength analysis. We base our inferences on the assumption that both the strength (X) and stress (Y) random variables follow a UTNBD with identical first shape and scale parameters. In the presence of a progressive type-II censoring scheme, we employ maximum likelihood, two parametric bootstrap methods, and Bayesian estimation approaches to derive the estimators. Due to the complexity introduced by censoring, the estimators are not available in explicit forms and are instead obtained through numerical approximation techniques. Furthermore, we compute the highest posterior density credible intervals and determine the asymptotic variance-covariance matrix. To assess the performance of our proposed estimators, we conduct a Monte Carlo simulation study and provide a comparative analysis. Finally, we illustrate the practical applicability of our study with an engineering application.

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Prince Sattam bin Abdulaziz Universities

Publisher

MDPI AG

Subject

Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis

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