Inference of the Exponentiated Rayleigh Distribution on Step-Stress Accelerated Life Testing under Type-I Hybrid Censored Data with Physical Application

Abstract

This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model. The maximum likelihood and Bayes methods estimate the parameters and acceleration factor. The parameters’ approximate confidence intervals are created. The Bayes estimates of the parameters for the squared error and linear exponential loss functions are computed using the Markov Chain Monte Carlo (MCMC) method. Finally, we perform a simulation study to evaluate the effectiveness of the proposed estimators. We provide a real-life data example (Strength data measurement in GPA, for single and impregnated carbon fibers) to explain the obtained results.OPEN ACCESS Received: 15/04/2025 Accepted: 21/08/2025 Published: 27/10/2025

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