Tools
Tools
Cawley, Gavin C. 
ORCID: https://orcid.org/0000-0002-4118-9095, Talbot, Nicola L. C., Janacek, Gareth J. and Peck, Mike W.
(2004)
Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis.
In:
Deterministic and Statistical Methods in Machine Learning.
Lecture Notes in Computer Science, 3635
.
Springer Berlin / Heidelberg, pp. 37-55.
Abstract
Survival analysis is a branch of statistics concerned with the time elapsing before “failure”, with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. In this paper we introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, that is the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques, but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 08 May 2011 13:32 |
| Last Modified: | 06 Aug 2023 00:47 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/21610 |
| DOI: | 10.1007/11559887_3 |
Actions (login required)
 |
View Item |