Statistical Inference for Gumbel Type-II Distribution Under Simple Step-Stress Life Test Using Type-II Censoring

In this paper, we focus on the parametric inference based on the tampered random variable model for simple step-stress life testing using Type-II censored data. The baseline lifetime of the experimental units, under normal stress conditions, follows the Gumbel Type-II distribution with a and k being the shape and scale parameters, respectively. Maximum likelihood estimator and Bayes estimator of the model parameters are derived based on Type-II censored samples. We obtain asymptotic intervals of the unknown parameters using the observed Fisher information matrix. Bayes estimators are obtained using Markov Chain Monte Carlo method under squared error loss and LINEX loss functions. We also construct highest posterior density intervals of the unknown model parameters. Extensive simulation studies are performed to investigate the finite sample properties of the proposed estimators. Three different optimality criteria have been considered to determine the optimal censoring plans. Finally, the methods are illustrated with the analysis of two real data sets.