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Found 5 papers, 0 papers with code
A general framework for the analysis of kernel-based tests
Kernel-based tests provide a simple yet effective framework that use the theory of reproducing kernel Hilbert spaces to design non-parametric testing procedures.
A Multiple kernel testing procedure for non-proportional hazards in factorial designs
In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e. g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously.
A kernel test for quasi-independence
We consider settings in which the data of interest correspond to pairs of ordered times, e. g, the birth times of the first and second child, the times at which a new user creates an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial.
Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior
In this paper, we prove almost surely consistency of a Survival Analysis model, which puts a Gaussian process, mapped to the unit interval, as a prior on the so-called hazard function.
Statistics Theory Statistics Theory
Gaussian Processes for Survival Analysis
We introduce a semi-parametric Bayesian model for survival analysis.
