Articles

Inference on stress-strength reliability based on progressively type-II censored data from two-parameter exponential distribution

Abstract

Introduction: Stress-strength models has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, biology, genetics, medicine etc. This paper investigates estimation of the stress-strength reliability parameter  in two-parameter exponential distributions under progressively type-II censored samples.

Methods: The maximum likelihood and the best linear unbiased estimates of  are obtained, and the Bayes estimates of  are computed under the squared error, linear-exponential, and Stein loss functions. Also, confidence intervals of stress-strength reliability such as the bootstrap confidence intervals, highest posterior density credible interval, and confidence interval based on the generalized pivotal quantity are obtained. Results: Using a simulation study, the point estimators and confidence intervals are evaluated and compared. A set of real data is presented for better clarification of the issue.

Conclusion: The results demonstrated that with increasing the sample size, in almost cases the ERs of all the estimators decrease. Also, in almost all cases the Bayes estimator under the LINEX loss function has smaller ER than the other estimators. Based on our simulation, the ELs of all intervals tend to decrease when the sample size increases. Moreover, the HPD confidence intervals are shorter than the others intervals for all the values of .

 

 

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IssueVol 10 No 1 (2024) QRcode
SectionArticles
DOI https://doi.org/10.18502/jbe.v10i1.17154
Keywords
Generalized pivotal quantity, Bayesian estimation, Gibbs sampling, Progressively type-ΙΙ censoring, Two-parameter exponential distribution
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How to Cite
1.
Rostamian S. Inference on stress-strength reliability based on progressively type-II censored data from two-parameter exponential distribution. JBE. 2024;10(1):64-81.