Bayesian nonparametric estimation of survival functions with multiple-samples information
Abstract
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.
Cite
@article{arxiv.1704.07645,
title = {Bayesian nonparametric estimation of survival functions with multiple-samples information},
author = {Alan Riva Palacio and Fabrizio Leisen},
journal= {arXiv preprint arXiv:1704.07645},
year = {2018}
}