# Bayesian nonparametric estimation of survival functions with multiple-samples information

## TL;DR

This paper introduces a Bayesian nonparametric approach for estimating survival functions that accounts for dependence among multiple samples, extending previous models to higher dimensions with theoretical and practical validation.

## Contribution

It develops a flexible, dependent vector of nonparametric priors for survival analysis, extending existing models to arbitrary dimensions with theoretical insights.

## Key findings

- Model effectively captures dependence among groups
- Theoretical results on posterior behavior are established
- Performance validated on simulated Clayton copula data

## 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.