Different estimation methods for exponentiated Rayleigh distribution under constant-stress accelerated life test

The accelerated life testing is an efficient approach and has been used in several fields to obtain failure time data of test units in a much shorter time than testing at normal operating conditions. In this article, we consider 6 frequentist estimation methods, namely, method of maximum likelihood estimation, method of least square estimation, method of weighted least square estimation, method of percentile estimation, method of maximum product of spacing estimation, and method of Cramer-von-Mises estimation to estimate the parameters and the reliability function of the exponentiated Rayleigh distribution under normal use conditions using constant accelerated life testing. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation for both small and large samples. The performances of the estimators have been compared in terms of their mean squared error using simulated samples. Simulation results showed that percentile method gives the best results among other estimation methods in terms of mean squared error. Finally, a real data set is analyzed for illustrative purposes.