Inferences Under Step-Stress Partially Accelerated Life Tests for Linear Exponential Distribution Based on Progressive Type-II Censored Data

In this paper, estimation problem for the parameters of linear exponential distribution based on step-stress partially accelerated life test under progressive Type-II censoring schemes is considered. To obtain the estimation for the distribution parameters and acceleration factor the maximum likelihood and Bayesian methods are used for this purpose. In addition, observed Fisher information matrix to find the confidence intervals of the model parameters is derived. By using Markov chain Monte Carlo method, approximate Bayes estimates under loss functions are obtained. Finally, a simulation study and a numerical example for illustration are conducted.