  In many real problems, dependence structures more general than
exchangeability are required. For instance, in some settings partial
exchangeability is a more reasonable assumption. For this reason, vectors of
dependent Bayesian nonparametric priors have recently gained popularity. They
provide flexible models which are tractable from a computational and
theoretical point of view. In this paper, we focus on their use for estimating
survival functions with multiple-samples information. Our methodology allows to
model the dependence among survival times of different groups of observations
and extend previous work to an arbitrary dimension . Theoretical results about
the posterior behaviour of the underlying dependent vector of completely random
measures are provided. The performance of the model is tested on a simulated
dataset arising from a distributional Clayton copula.
