Statistical inference of reliability in multicomponent stress strength model for pareto distribution based on upper record values. (4th March 2022) - Azhad, Qazi J.; Arshad, Mohd.; Khandelwal, Nancy
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.