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Publication Detail
A maximum-mean-discrepancy goodness-of-fit test for censored data
  • Publication Type:
  • Authors:
    Fernández T, Gretton A
  • Publisher:
    Proceedings of Machine Learning Research
  • Publication date:
  • Published proceedings:
    Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics
  • Name of conference:
    22nd International Conference on Artificial Intelligence and Statistics
  • Conference place:
    Naha, Okinawa, Japan
  • Conference start date:
  • Conference finish date:
  • Keywords:
    stat.ME, stat.ME
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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