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2018 Bayesian nonparametric estimation of survival functions with multiple-samples information
Alan Riva Palacio, Fabrizio Leisen
Electron. J. Statist. 12(1): 1330-1357 (2018). DOI: 10.1214/18-EJS1420

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 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.

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Alan Riva Palacio. Fabrizio Leisen. "Bayesian nonparametric estimation of survival functions with multiple-samples information." Electron. J. Statist. 12 (1) 1330 - 1357, 2018. https://doi.org/10.1214/18-EJS1420

Information

Received: 1 April 2017; Published: 2018
First available in Project Euclid: 3 May 2018

zbMATH: 06875402
MathSciNet: MR3797716
Digital Object Identifier: 10.1214/18-EJS1420

Subjects:
Primary: 60G57 , 62F15
Secondary: 60G51

Keywords: Bayesian nonparametrics , Dependent completely random measures , Survival analysis

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Vol.12 • No. 1 • 2018
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