Power Rayleigh Accelerated Life Model Inference with Censoring: Methods and Applications

In reliability engineering research, obtaining accurate information about the life expectancy of products or materials is essential. However, collecting such data under normal operating conditions is often challenging, particularly for highly reliable items. This paper addresses the problem of statistical inference for lifetime data following the power Rayleigh distribution. To reduce experimental cost and time, a partially step-stress-accelerated life test is employed under a Type-I generalized hybrid censoring scheme (GHCS). Point estimators of the model parameters, as well as the acceleration factor, are derived using both maximum likelihood and Bayesian approaches. Furthermore, interval estimation is developed based on the asymptotic normality of maximum likelihood estimators, in addition to a bootstrap method and Markov-chain Monte Carlo techniques. A real-life dataset is analyzed to demonstrate the applicability of the proposed model. Finally, a Monte Carlo simulation study is conducted to evaluate and compare the performance of the suggested model and estimation procedures.