Inference for a constant-stress model under progressive type-II censored data from the truncated normal distribution

In this study, constant-stress accelerated life testing has been investigated using type-II censoring of failure data from a truncated normal distribution. Various classical estimation approaches are discussed for estimating model parameters, hazard rates, and reliability functions. Among these methods are maximum likelihood estimation, the EM algorithm, and maximum product spacing estimation. Interval estimation is also introduced in the context of asymptomatic confidence intervals and bootstrap intervals. Furthermore, the missing information principle was employed to compute the observed Fisher information matrix. Three optimality criteria linked with the Fisher information matrix are considered to find out the optimal value of each stress level. To interpret the proposed techniques, Monte Carlo simulations are run in conjunction with real data analysis.