no code implementations • 31 Aug 2022 • Tamara Fernández, Nicolás Rivera
Kernel-based tests provide a simple yet effective framework that use the theory of reproducing kernel Hilbert spaces to design non-parametric testing procedures.
no code implementations • 15 Jun 2022 • Marc Ditzhaus, Tamara Fernández, Nicolás Rivera
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.
no code implementations • NeurIPS 2020 • Tamara Fernández, Wenkai Xu, Marc Ditzhaus, Arthur Gretton
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.
no code implementations • 7 Nov 2016 • Tamara Fernández, Yee Whye Teh
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
no code implementations • NeurIPS 2016 • Tamara Fernández, Nicolás Rivera, Yee Whye Teh
We introduce a semi-parametric Bayesian model for survival analysis.