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Gaussian processes for survival analysis

Abstract:

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival anal...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Institution:
University of Oxford
Oxford college:
University College
Role:
Author
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Funding agency for:
Fernandez, T
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Funding agency for:
Teh, Y
Grant:
617071
Publisher:
Curran Associates Publisher's website
Volume:
29
Pages:
5021-5029
Host title:
Advances in Neural Information Processing Systems 29: 30th Annual Conference on Neural Information Processing Systems 2016
Publication date:
2016-12-05
Acceptance date:
2016-09-04
Event location:
Barcelona, Spain
Event start date:
2016-12-05T00:00:00Z
Event end date:
2016-12-08T00:00:00Z
ISSN:
1049-5258
Source identifiers:
661533
ISBN:
9781510838819
Pubs id:
pubs:661533
UUID:
uuid:db03c8ae-6e6b-4fdd-8c3d-edd217113b47
Local pid:
pubs:661533
Deposit date:
2016-11-24

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