Statistical inference of reliability in multicomponent stress strength model for pareto distribution based on upper record values. (4th March 2022)

• Record Type: Journal Article
• Authors: Azhad, Qazi J.; Arshad, Mohd.; Khandelwal, Nancy
• Is Part Of: International journal of modelling & simulation. Volume 42:Number 2(2022)
• Page Start: 319
• Page End: 334
• Publication Date: 2022-03-04
• Subjects: Pareto distribution -- multicomponent stress-strength -- uniformly minimum variance unbiased estimator -- lindley approximation -- markov Chain Monte Carlo -- bootstrapping; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2021.1891496
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: In this article, inferences about the multicomponent stress strength reliability are drawn under the assumption that strength and stress follow independent Pareto distribution with different shapes ( α 1, α 2 ) and common scale parameter θ under the setup of upper record values. The maximum likelihood estimator, Bayes estimator under-squared error and Linear exponential loss function, of multicomponent stress-strength reliability are constructed with corresponding highest posterior density interval for unknown θ . For known θ, uniformly minimum variance unbiased estimator and asymptotic distribution of multicomponent stress-strength reliability with asymptotic confidence interval is discussed. Also, various Bootstrap confidence intervals are constructed. A simulation study is conducted to numerically compare the performances of various estimators of multicomponent stress-strength reliability. Finally, a real life example is presented to show the applications of derived results in real life scenarios. Several researchers have attempted such problems under different distributions e.g., [1] considered the Weibull distribution for estimation of multicomponent stress-strength reliability, [2] considered generalized Rayleigh distribution for the reliability estimation of multicomponent stress-strength setup.