The Bayesian quantile regression method in accelerated life tests

In accelerated life tests (ALT), there are many limitations for a traditional parametric method to estimate a particular quantile in the lower tail of the lifetime distribution at the use condition. This article develops a Bayesian quantile regression method for analyzing ALT data. As a distribution-free method, Bayesian quantile regression method is robust to lifetime distribution uncertainty and flexible to deal with the relationship between shape parameter and accelerating stresses. A Gibbs sampling method is implemented for Bayesian posterior inference. This method is easy to implement and allows us to use data augmentation to simplify the computation in the presence of right-censored observations. A simulation study and a real example are used to demonstrate the advanced properties of proposed method.