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


Tamara Fernandez, Arthur Gretton ;
Proceedings of Machine Learning Research, PMLR 89:2966-2975, 2019.


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.

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