Estimation of the Generalized Rayleigh Distribution Parameters Based on the Dual Generalized Progressive Hybrid Censoring Scheme

Several researchers advocate the generalized Rayleigh distribution as a lifetime model, considering the problem of estimating the distribution parameters based on the classical and Bayesian methods using the complete and censored data. Therefore, the novelty of this work is applying new numerical iteration techniques, such as the Picard and the Runge-Kutta methods for estimating the distribution parameters and comparing them to the Bayes’ method via Monte Carlo simulations. The simulation results indicated that the numerical methods provide better estimates and outperform the Bayes’ method based on the dual generalized progressive hybrid censoring scheme. Finally, two real datasets have been analyzed for illustration and comparison of the proposed methods.