Fong, Edwin;
Lehmann, Brieuc;
(2022)
A Predictive Approach to Bayesian Nonparametric Survival Analysis.
In: Camps-Valls, G and Ruiz, FJR and Valera, I, (eds.)
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 6990-7013).
PMLR 151
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Abstract
Bayesian nonparametric methods are a popular choice for analysing survival data due to their ability to flexibly model the distribution of survival times. These methods typically employ a nonparametric prior on the survival function that is conjugate with respect to right-censored data. Eliciting these priors, particularly in the presence of covariates, can be challenging and inference typically relies on computationally intensive Markov chain Monte Carlo schemes. In this paper, we build on recent work that recasts Bayesian inference as assigning a predictive distribution on the unseen values of a population conditional on the observed samples, thus avoiding the need to specify a complex prior. We describe a copula-based predictive update which admits a scalable sequential importance sampling algorithm to perform inference that properly accounts for right-censoring. We provide theoretical justification through an extension of Doob’s consistency theorem and illustrate the method on a number of simulated and real data sets, including an example with covariates. Our approach enables analysts to perform Bayesian nonparametric inference through only the specification of a predictive distribution.
Type: | Proceedings paper |
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Title: | A Predictive Approach to Bayesian Nonparametric Survival Analysis |
Event: | International Conference on Artificial Intelligence and Statistics |
Location: | ELECTR NETWORK |
Dates: | 28 Mar 2022 - 30 Mar 2022 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://proceedings.mlr.press/v151/fong22a.html |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Science & Technology, Technology, Physical Sciences, Computer Science, Artificial Intelligence, Statistics & Probability, Computer Science, Mathematics, MODEL |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10159019 |
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