Exact tests for the means of Gaussian stochastic processes

This paper investigates the inferential properties of testing the means of Gaussian functional data, using a Mahalanobis type distance for Hilbert spaces. We establish the analytic power of exact and asymptotic tests, for the known and unknown covariance case, respectively.