We study weak convergence of empirical processes of dependent data, indexed by classes of functions. We obtain results that are especially suitable for data arising from dynamical systems and Markov chains, where the Central Limit Theorem for partial sums is commonly derived via the spectral gap technique. Our results apply, e.g. to the empirical process of ergodic torus automorphisms.

While effective concentration inequalities for suprema of empirical processes exist under boundedness or strict tail assumptions, no comparable results have been available under considerably weaker assumptions. In this paper, we derive concentration inequalities assuming only low moments for an envelope of the empirical process. These concentration inequalities are beneficial even when the envelope is much larger than the single functions under consideration.

"We study the problem of so-called "exact" or non-asymptotic deviations between a reference measure $\mu$ and its empirical version $L_n$, in the $p$-Wasserstein metric, $1 \leq p \leq 2$, under the standing assumption that $\mu$ satisfies a transport-entropy inequality. This work is a generalization of an article by F.Bolley, A.Guillin and C.Villani, where the case of measures with support in $\R^d$ was studied. Our methods are based on concentration inequalities and extend to the general setting of measures on a Polish space. Deviation bounds for the occupation measure of a contracting Markov chain in $W_1$ distance are also given. Throughout the text, several examples are worked out, including the cases of Gaussian measures on separable Banach spaces, and laws of diffusion processes."

"For stochastic processes $\{X_t: t \in E\}$, we establish sufficient conditions for the empirical process based on $\{ I_{X_t \le y} - P(X_t \le y): t \in E, y \in \mathbb{R}\}$ to satisfy the CLT uniformly in $ t \in E, y \in \mathbb{R}$. Corollaries of our main result include examples of classical processes where the CLT holds, and we also show that it fails for Brownian motion tied down at zero and $E= [0,1]$."