Fernández, T;
Rivera, N;
(2021)
A reproducing kernel Hilbert space log-rank test for the two-sample problem.
Scandinavian Journal of Statistics: Theory and Applications
, 48
(4)
pp. 1384-1432.
10.1111/sjos.12496.
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Abstract
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 considered to make them more robust against a broader family of alternatives, including taking linear combinations, or the maximum among a finite collection of them. In this article, we propose as test statistic the supremum of a collection of (potentially infinitely many) weighted log‐rank tests where the weight functions belong to the unit ball in a reproducing kernel Hilbert space (RKHS). By using some desirable properties of RKHSs we provide an exact and simple evaluation of the test statistic and establish connections with previous tests in the literature. Additionally, we show that for a special family of RKHSs, the proposed test is omnibus. We finalize by performing an empirical evaluation of the proposed methodology and show an application to a real data scenario.
| Type: | Article |
|---|---|
| Title: | A reproducing kernel Hilbert space log-rank test for the two-sample problem |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/sjos.12496 |
| Publisher version: | https://doi.org/10.1111/sjos.12496 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | log‐rank test, reproducing kernel Hilbert space, right‐censored data, survival analysis, two‐sample tests |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10110930 |
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