Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications.

ABSTRACT: This article examines the estimate of ϑ = P [T < Q], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. Assume that the stress (T) and strength (Q) are independent random variables that follow the Burr III distribution and the Burr XII distribution, respectively, with a common first-shape parameter. The Bayes estimator and maximum likelihood estimator of ϑ are obtained. The maximum likelihood (ML) estimator is obtained for non-Bayesian estimation, and the accompanying confidence interval is constructed using the delta approach and the asymptotic normality of ML estimators. Through the use of non-informative and gamma informative priors, the Bayes estimator of ϑ under squared error and linear exponential loss functions is produced. It is sugge

SUBMITTER: Alyami SA 

PROVIDER: S-EPMC11661653 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature