Inferences of accelerated generalized type-I hybrid censoring data under power hazard rate population

In reliability engineering or medical studies, more information about the life expectancy of products or materials is needed. This information under use conditions is more difficult to collect especially under highly reliable case. In this paper, the problem of statistical inference when the lifetime of a product or material has the power failure rate distribution is developed. To save the cost and time induced by units the experiment is designed with respect to partially step-stress accelerated life tests (ALTs) with type-I generalized hybrid censoring scheme (GHCS). The point estimate of the model parameters as well as the accelerated factor are obtained with respect to the maximum likelihood (ML) and Bayes methods. Also, the asymptotic normal properties of ML estimates, two bootstrap techniques and Markov Chain Monte Carlo techniques are used to formulate interval estimators. A real lifetime data set is used to illustrate the application of the proposed model. Finally, a Monte Carlo simulation study is constructed to discuss and assess, the proposed model and estimation methods.