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 Tampered Random Variable (TRV) model for simple step-stress life testing (SSLT) using Type-II censored data. The baseline lifetime of experimental units, under normal stress conditions, follows Gumbel distribution with $\alpha$ and $\lambda$ being shape scale parameters, respectively. Maximum likelihood estimator (MLE) Bayes parameters are derived samples. We obtain asymptotic intervals unknown observed Fisher information matrix. estimators obtained Markov Chain Monte Carlo (MCMC) method squared error loss LINEX functions. also construct highest posterior density (HPD) parameters. Extensive simulation studies performed to investigate finite sample properties proposed estimators. Three different optimality criteria have been considered determine optimal censoring plans. Finally, methods illustrated analysis two real data sets.