Parametric Inference for Step-Stress Accelerated Life Testing From Rayleigh Distribution Under Ordered Ranked Set Sampling

The inference step-stress accelerated life test under ranked set sampling comes naturally for cost or money constraints. Here, in this article, we develop a Bayesian analysis in the context of accelerated life tests to derive the Bayes estimates of the unknown parameters of the Rayleigh distribution when the data are ordered ranked set sample. The Bayes estimates are derived under squared error loss, general entropy loss, and Al-Bayyati loss functions. A Monte Carlo simulation study and numerical computations are carried out to study the precision of the Bayes estimates and to obtain average interval lengths of the confidence intervals.