Statistical inference of step-stress partially accelerated life tests for the Chris-Jerry distribution under type-I censored data with engineering applications

In this article, we focus on the parametric inference of the Chris-Jerry distribution under Type-I censoring using the tampered random variable model within the framework of step-stress partially accelerated life tests. It is assumed that the lifetimes of the test units follow the Chris-Jerry distribution. Various estimation techniques are used to estimate the model parameters. In addition, asymptotic confidence intervals for the parameters are derived using the Fisher information matrix. The practical applicability of the proposed methods is demonstrated through the analysis of a real data set involving AT-II PHCS and the breaking stress of carbon fibers. Numerical results based on Markov Chain Monte Carlo simulations indicate that the mean squared error, bias and relative absolute bias decrease as the sample size increases.