Statistical Inference and Optimal Design of Accelerated Life Testing for the Chen Distribution under Progressive Type-II Censoring

This paper discusses statistical inference and optimal design of constant-stress accelerated life testing for the Chen distribution under progressive Type-II censoring. The scale parameter is assumed to be a logarithmic linear function stress level. maximum likelihood estimates parameters are obtained. Then, observed Fisher information matrix derived utilized construct asymptotic confidence intervals. Meanwhile, parametric bootstrap methods provided interval estimation. In addition, Bayes squared error loss obtained by applying Tierney Kadane technique Lindley’s approximation. As design, D- A-optimality criteria considered determine transformed Finally, simulation carried out demonstrate proposed estimation techniques criteria, real data set discussed.