Analysis of the Generalized Progressive Hybrid Censoring Technique from Exponentiated Exponential Stress Strength Model

The statistical inference of R = P (X < Y < Z ) for a component when it is exposed to two independent stresses and it having one strength for exponentiared exponential distribution is regarded. We assume that both stresses and strength variables follow exponenti-ated exponential distribution with a common scale parameter. Maximum likelihood and Bayesian techniques are used to get various point and interval estimators for the reliability model via generalized progressive hybrid censoring samples. This scheme can reduce total test time as well as costs, resulting in improved statistical analysis efficiency. Furthermore, Bayesian estimators for symmetric such as squared error, and asymmetric loss functions such as linear exponential are derived. We propose using Markov chain Monte Carlo approach due to complicated forms of Bayesian estimator. Asymptotic distribution theory is used to calculate the confidence intervals. We further compute Bayes estimates along with credible intervals. The Monte Carlo simulation is used to compare the efficiency of the proposed estimations. Finally, for example reasons, a progressively censored real-data set is shown.