Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 12, Number 1 (2018), 1330-1357.
Bayesian nonparametric estimation of survival functions with multiple-samples information
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 multivariate survival functions. Our model extends the work of Epifani and Lijoi (2010) to an arbitrary dimension and allows to model the dependence among survival times of different groups of observations. 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.
Electron. J. Statist., Volume 12, Number 1 (2018), 1330-1357.
Received: April 2017
First available in Project Euclid: 3 May 2018
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62F15: Bayesian inference 60G57: Random measures
Secondary: 60G51: Processes with independent increments; Lévy processes
Riva Palacio, Alan; Leisen, Fabrizio. Bayesian nonparametric estimation of survival functions with multiple-samples information. Electron. J. Statist. 12 (2018), no. 1, 1330--1357. doi:10.1214/18-EJS1420. https://projecteuclid.org/euclid.ejs/1525334453