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A maximum-mean-discrepancy goodness-of-fit test for censored data

Fernández, T; Gretton, A; (2019) A maximum-mean-discrepancy goodness-of-fit test for censored data. In: Chaudhuri, K and Sugiyama, M, (eds.) Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. (pp. pp. 2966-2975). Proceedings of Machine Learning Research: Naha, Okinawa, Japan. Green open access

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Abstract

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 industrial life-testing. The test statistic is straightforward to compute, as is the test threshold, and we establish consistency under the null. Unlike earlier approaches such as the Log-rank test, we make no assumptions as to how the data distribution might differ from the null, and our test has power against a very rich class of alternatives. In experiments, our test outperforms competing approaches for periodic and Weibull hazard functions (where risks are time dependent), and does not show the failure modes of tests that rely on user defined features. Moreover, in cases where classical tests are provably most powerful, our test performs almost as well, while being more general.

Type: Proceedings paper
Title: A maximum-mean-discrepancy goodness-of-fit test for censored data
Event: 22nd International Conference on Artificial Intelligence and Statistics
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v89/fernandez19a.html
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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/10076530
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