Bayesian nonparametric estimation of survival functions with multiple-samples information. (English) Zbl 1473.62329

Summary: 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 I. Epifani and A. Lijoi [Stat. Sin. 20, No. 4, 1455–1484 (2010; Zbl 1200.62121)] 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.


62N02 Estimation in survival analysis and censored data
62G05 Nonparametric estimation
60G51 Processes with independent increments; Lévy processes
60G57 Random measures


Zbl 1200.62121


Full Text: DOI arXiv Euclid


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