Asymptotics for posterior hazards
Abstract
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.
Cite
@article{arxiv.0908.1882,
title = {Asymptotics for posterior hazards},
author = {Pierpaolo De Blasi and Giovanni Peccati and Igor Prünster},
journal= {arXiv preprint arXiv:0908.1882},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/08-AOS631 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)