Classical and Bayesian Estimation of Stress-Strength Reliability from Generalized Inverted Exponential Distribution based on Upper Records

This paper deals with the problem of classical and Bayesian estimation of stress-strength reliability from a generalized inverted exponential distribution (GIED) based on upper record values. Hassan et al. (2018) have discussed the maximum likelihood estimator (MLE) and Bayes estimator of R by considering that the scale parameter of defined distribution is known while we have considered the case when all the parameters of GIED are unknown. In classical approach, we have obtained MLE and uniformly minimum variance estimator (UMVUE). In Bayesian approach, we have considered the Bayes estimator of R by considering the squared error loss function. Further, based on upper records, we have considered the asymptotic confidence interval (CI) based on MLE, Bayesian credible interval and bootstrap CI for R. Moreover, to evaluate the performances of the discussed estimators of R, a Monte Carlo simulation and a real data application have been carried out.