A Kernel Test for Three-Variable Interactions with Random Processes
This provides a statistical test for researchers analyzing dependencies in time series or sequential data, though it is incremental as it extends an existing method to a new context.
The paper tackles the problem of detecting three-variable interactions in stationary random processes, where existing permutation bootstrap methods fail, by applying a wild bootstrap method to the Lancaster interaction measure. The result shows that this approach outperforms existing tests in cases where two independent variables have weak individual influences but strong joint influence on a third variable.
We apply a wild bootstrap method to the Lancaster three-variable interaction measure in order to detect factorisation of the joint distribution on three variables forming a stationary random process, for which the existing permutation bootstrap method fails. As in the i.i.d. case, the Lancaster test is found to outperform existing tests in cases for which two independent variables individually have a weak influence on a third, but that when considered jointly the influence is strong. The main contributions of this paper are twofold: first, we prove that the Lancaster statistic satisfies the conditions required to estimate the quantiles of the null distribution using the wild bootstrap; second, the manner in which this is proved is novel, simpler than existing methods, and can further be applied to other statistics.