  An important issue in survival analysis is the investigation and the modeling
of hazard rates. Within a Bayesian nonparametric framework, a natural and
popular approach is to model hazard rates as kernel mixtures with respect to a
completely random measure. In this paper we provide a comprehensive analysis of
the asymptotic behavior of such models. We investigate consistency of the
posterior distribution and derive fixed sample size central limit theorems for
both linear and quadratic functionals of the posterior hazard rate. The general
results are then specialized to various specific kernels and mixing measures
yielding consistency under minimal conditions and neat central limit theorems
for the distribution of functionals.
