Combining nonparametric predictive inference and power-Weibull model for accelerated life testing

Accelerated life testing provides an interesting challenge for quantification of the uncertainties involved, in particular due to the required linking of components' failure times, or failure time distributions, at different stress levels. This paper provides an initial exploration of the use of statistical methods based on imprecise probabilities for accelerated life testing. We apply nonparametric predictive inference at the normal stress level, in combination with an estimated parametric power-Weibull model linking observations at different stress levels. To provide robustness with regard to this assumed link between different stress levels, we introduce imprecision by considering an interval around the parameter estimate, leading to observations at stress levels other than the normal level to be transformed to intervals at the normal level. The width of such intervals is increasing with the difference between the stress level at which an item is tested and the normal level. The resulting inference method is predictive, so it explicitly considers the random failure time of a future item tested at the normal level. To investigate the performance of our imprecise predictive method and to get insight into suitable amount of imprecision for the linking between levels, we aim to perform extensive simulation studies. Results of first simulation studies are briefly discussed. The paper concludes with a discussion of related research topics.