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Bayesian nonparametric estimation of survival functions with multiple-samples information

Riva Palacio, Alan, Leisen, Fabrizio (2018) Bayesian nonparametric estimation of survival functions with multiple-samples information. Electronic Journal of Statistics, 12 (1). pp. 1330-1357. ISSN 1935-7524. (doi:10.1214/18-EJS1420) (KAR id:66443)

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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.

Item Type: Article
DOI/Identification number: 10.1214/18-EJS1420
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Fabrizio Leisen
Date Deposited: 18 Mar 2018 13:49 UTC
Last Modified: 16 Feb 2021 13:53 UTC
Resource URI: (The current URI for this page, for reference purposes)
Leisen, Fabrizio:
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