Gaussian process regression for survival data with competing risks

@inproceedings{Barrett2014GaussianPR,
  title={Gaussian process regression for survival data with competing risks},
  author={James E. Barrett and Anthony C. C. Coolen},
  year={2014}
}
We apply Gaussian process (GP) regression, which provides a powerful non-parametric probabilistic method of relating inputs to outputs, to survival data consisting of time-to-event and covariate measurements. In this context, the covariates are regarded as the ‘inputs’ and the event times are the ‘outputs’. This allows for highly flexible inference of non-linear relationships between covariates and event times. Many existing methods, such as the ubiquitous Cox proportional hazards model, focus… CONTINUE READING

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