Gaussian process regression for survival data with competing risks

  title={Gaussian process regression for survival data with competing risks},
  author={James E. Barrett and Anthony C. C. Coolen},
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


Publications referenced by this paper.
Showing 1-10 of 35 references

Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

  • L Fahrmeir, T. Kneib
  • 2011
1 Excerpt

Similar Papers

Loading similar papers…