  A wild bootstrap method for nonparametric hypothesis tests based on kernel
distribution embeddings is proposed. This bootstrap method is used to construct
provably consistent tests that apply to random processes, for which the naive
permutation-based bootstrap fails. It applies to a large group of kernel tests
based on V-statistics, which are degenerate under the null hypothesis, and
non-degenerate elsewhere. To illustrate this approach, we construct a
two-sample test, an instantaneous independence test and a multiple lag
independence test for time series. In experiments, the wild bootstrap gives
strong performance on synthetic examples, on audio data, and in performance
benchmarking for the Gibbs sampler.
