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Publication Detail
A kernel test for quasi-independence
  • Publication Type:
  • Authors:
    Fernández T, Xu W, Ditzhaus M, Gretton A
  • Publisher:
    Neural Information Processing Systems Conference
  • Publication date:
  • Published proceedings:
    NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems
  • Volume:
  • Status:
  • Name of conference:
    NIPS'20: 34th International Conference on Neural Information Processing Systems
  • Conference place:
    Vancouver, Canada
  • Conference start date:
  • Conference finish date:
  • Print ISSN:
  • Language:
We consider settings in which the data of interest correspond to pairs of ordered times, e.g, the birth times of the first and second child, the times at which a new user creates an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial. In these settings, the two times are not independent (the second occurs after the first), yet it is still of interest to determine whether there exists significant dependence beyond their ordering in time. We refer to this notion as "quasi-(in)dependence". For instance, in a clinical trial, to avoid biased selection, we might wish to verify that recruitment times are quasi-independent of survival times, where dependencies might arise due to seasonal effects. In this paper, we propose a nonparametric statistical test of quasi-independence. Our test considers a potentially infinite space of alternatives, making it suitable for complex data where the nature of the possible quasi-dependence is not known in advance. Standard parametric approaches are recovered as special cases, such as the classical conditional Kendall’s tau, and log-rank tests. The tests apply in the right-censored setting: an essential feature in clinical trials, where patients can withdraw from the study. We provide an asymptotic analysis of our test-statistic, and demonstrate in experiments that our test obtains better power than existing approaches, while being more computationally efficient.
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