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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 shapesand common scale parameterunder 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 unknownFor knownuniformly 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.

Statistical inference of reliability in multicomponent stress strength model for pareto distribution based on upper record values