Open Access
Translator Disclaimer
2017 Exchangeable Markov survival processes and weak continuity of predictive distributions
Walter Dempsey, Peter McCullagh
Electron. J. Statist. 11(2): 5406-5451 (2017). DOI: 10.1214/17-EJS1381


We study exchangeable, Markov survival processes – stochastic processes giving rise to infinitely exchangeable non-negative sequences $(T_{1},T_{2},\ldots)$. We show how these are determined by their characteristic index $\{\zeta_{n}\}_{n=1}^{\infty}$. We identify the harmonic process as the family of exchangeable, Markov survival processes that compose the natural set of statistical models for time-to-event data. In particular, this two-dimensional family comprises the set of exchangeable, Markov survival processes with weakly continuous predictive distributions. The harmonic process is easy to generate sequentially, and a simple expression exists for both the joint probability distribution and multivariate survivor function. We show a close connection with the Kaplan-Meier estimator of the survival distribution. Embedded within the process is an infinitely exchangeable ordered partition. Aspects of the process, such as the distribution of the number of blocks, are investigated.


Download Citation

Walter Dempsey. Peter McCullagh. "Exchangeable Markov survival processes and weak continuity of predictive distributions." Electron. J. Statist. 11 (2) 5406 - 5451, 2017.


Received: 1 April 2017; Published: 2017
First available in Project Euclid: 28 December 2017

zbMATH: 06825051
MathSciNet: MR3743735
Digital Object Identifier: 10.1214/17-EJS1381

Keywords: Bayesian nonparametrics , characteristic index , Gibbs-type splitting rules , infinite exchangeability , Survival analysis


Vol.11 • No. 2 • 2017
Back to Top