Semiparametric inference for the accelerated failure time model using hierarchical mixtures with generalised gamma processes

We adopt a Bayesian semiparametric approach for an accelerated failure time model, when the error distribution is a mixture of parametric densities on the positive reals with a (normalized) generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses the Dirichlet process mixture (DPM) model, but it is more flexible in the detection of clusters in the data, as far as density estimation is concerned. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters, for real and simulated datasets.