A Sharp Rate of Convergence in the Functional Central Limit Theorem with Gaussian Input

Abstract: When the underlying random variables are Gaussian, the classical Central Limit Theorem (CLT) is trivial, but the functional CLT is not. The objective of the paper is to investigate the functional CLT for stationary Gaussian processes in the Wasserstein-1 metric on the space of continuous functions. Matching upper and lower bounds are established, indicating that the convergence rate is slightly faster than in the L\'{e}vy-Prokhorov metric.