In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter $k$ is known, the maximum likelihood estimator (MLE), the approximate Bayes estimator and ...

Abstact:The term “stressstrength reliability” when it appears in statistical literature typically refers to the quantityP(X > Y), where a system with random strength X is subjected to a random strength Y such that a system fails, if the stress exceeds the strength. Stress –Strength reliability is considered in this paper where the strength follows finite mixture of two parameter Lindley distrib...

A multicomponent system of k components having strengths following k– independently and identically distributed random variables and each component experiencing a random stress Y is considered. The system is regarded as alive only if at least s out of k (s< k) strengths exceed the stress. The reliability of such a system is obtained when strength, stress variates are given by inverse Rayleigh d...

For many technical systems, stress-strength models are of special importance. Stress-strength models can be described as an assessment of the reliability of the component in terms of X and Y random variables where X is the random ”stress” experienced by the component and Y is the random ”strength” of the component available to overcome the stress. The reliability of the component is the probabi...

This article deals with estimating an extension of the well-known stressstrength reliability in nonparametric setup. By means of Monte Carlo simulations, the proposed estimator is compared with its parametric analogs in the case of exponential distribution. The results show that the estimator could be highly efficient in many situations considered.