33 citations · 49 across the 6 of their papers we have counts for
6 papers
Composite Goodness-of-fit Tests with Kernels
Oscar Key, Arthur Gretton, François-Xavier Briol +1
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly…
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez, Nicolas Rivera, Wenkai Xu +1
Survival Analysis and Reliability Theory are concerned with the analysis of time-to-event data, in which observations correspond to waiting times until an event of interest such as…
A kernel log-rank test of independence for right-censored data
Tamara Fernandez, Arthur Gretton, David Rindt +1
We introduce a general non-parametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretatio…
A Reproducing Kernel Hilbert Space log-rank test for the two-sample problem
Tamara Fernandez, Nicolas Rivera
Weighted log-rank tests are arguably the most widely used tests by practitioners for the two-sample problem in the context of right-censored data. Many approaches have been conside…
A maximum-mean-discrepancy goodness-of-fit test for censored data
Tamara Fernández, Arthur Gretton
We introduce a kernel-based goodness-of-fit test for censored data, where observations may be missing in random time intervals: a common occurrence in clinical trials and industria…
Gaussian Processes for Survival Analysis
Tamara Fernández, Nicolás Rivera, Yee Whye Teh
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from…