Sharp adaptive and pathwise stable similarity testing for scalar ergodic diffusions

Abstract

Within the nonparametric diffusion model, we develop a multiple test to infer about similarity of an unknown drift b to some reference drift ${\mathit{b}}_0$: At prescribed significance, we simultaneously identify those regions where violation from similarity occurs, without a priori knowledge of their number, size and location. This test is shown to be minimax-optimal and adaptive. At the same time, the procedure is robust under small deviation from Brownian motion as the driving noise process. A detailed investigation for fractional driving noise, which is neither a semimartingale nor a Markov process, is provided for Hurst indices close to the Brownian motion case.