Estimation in constant-stress accelerated life tests for extension of the exponential distribution under progressive censoring

Abstract

In this paper, constant-stress accelerated life test is assumed when the lifetime of test units follows an extension of the exponential distribution. Based on progressive censoring, the maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are obtained. The BEs are obtained based on both non-informative and informative priors. In addition, the approximate, bootstrap and credible confidence intervals (CIs) of the estimators are constructed. Moreover, a real dataset is analyzed to illustrate the proposed procedures. Furthermore, the real dataset is used to show that extension of the exponential distribution can be a better model than Weibull distribution and generalized exponential distribution. Finally, simulation studies are carried out to investigate the accuracy of the MLEs and BEs for the parameters involved.