Title: Non-asymptotic theory for nonparametric testing,
Abstract: We consider nonparametric testing in a non-asymptotic framework. Our
statistical guarantees are exact in the sense that Type I and II errors are
controlled for any finite sample size. Meanwhile, one proposed test is shown to
achieve minimax optimality in the asymptotic sense. An important consequence of
this non-asymptotic theory is a new and practically useful formula for
selecting the optimal smoothing parameter in nonparametric testing. The leading
example in this paper is smoothing spline models under Gaussian errors. The
results obtained therein can be further generalized to the kernel ridge
regression framework under possibly non-Gaussian errors. Simulations
demonstrate that our proposed test improves over the conventional asymptotic
test when sample size is small to moderate.