Nonparametric Bayesian mixture modelling for failure time data

We fit a Bayesian semiparametric accelerated failure time mixed-effects model to a classical Kevlar fibre lifetime dataset (with censoring). The error is a shape-scale mixture of Weibull densities, mixed by a normalized generalized gamma random measure, encompassing the Dirichlet process. We implement an MCMC scheme, obtaining posterior credibility intervals for the predictive distributions and for the quantiles of the failure times under different stress levels. Random spool effects are taken up by the nonparametric mixture, where every component accounts for a different spool. Compared to previous analyses, we obtain narrower credibility intervals and a better fit to the data.