Bayesian analysis for step-stress accelerated life testing under progressive interval censoring

A step-stress accelerated life testing model is considered for progressive type-I censored experiments when the tested items are not monitored continuously but inspected at prespecified time points, producing thus grouped data. The underlying lifetime distributions belong to a general scale family of distributions. The points of stress-level change are simultaneously inspection points as well while there is the option of assigning additional inspection points in between the stress-level change points. In a Bayesian framework, the posterior distributions of the parameters of the model are derived for characteristic choices of prior distributions, as conjugate-like and normal priors; vague or noninformative. The developed approach is illustrated on a simulated example and on a real data set, both known from the literature. The results are compared to previous analyses; frequentist or Bayes.