Inference for Simple Step Stress Accelerated Life Test Model Under Progressively Censored Gompertz Data

ABSTRACT In this article analysis of a simple step‐stress accelerated life test is considered under progressive type‐II censoring. A cumulative exposure model is considered when the latent lifetimes of test units follow the Gompertz distribution with different shape parameters and a common scale parameter. We explore the study by estimating all unknown parameters using classical and Bayesian techniques. The model parameters are estimated using maximum likelihood and Bayesian methods. Subsequently, interval estimates are derived based on the observed Fisher information matrix. Bayesian estimates are obtained using squared error and linear exponential loss functions. Subsequently highest posterior density intervals are also constructed. We examine the efficiency of all estimators through simulation studies. Finally, we provide a real‐life example in support of the considered model.