Multicomponent stress strength reliability estimation for Pareto distribution based on upper record values

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. 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.

Estimation of reliability function based on the upper record values for generalized gamma Lindley stress–strength model: Case study COVID-19

“…For more explanation, there are a lot of researchers who studied this model. Jamal et al (2019) studied the reliability function based on the model of stressstrength with the Pareto distribution function in a multi-component system. The stress-strength model of reliability with truncated hazard rate was studied by Bai et al (2019).…”