A Central Limit Theorem Under Metric Entropy with $L_2$ Bracketing

Let $(\mathbf{S}, \rho)$ be a metric space, $(\mathbf{V}, \mathscr{V}, \mu)$ be a probability space, and $f: \mathbf{S} \times \mathbf{V} \rightarrow \mathbb{R}$ be a real-valued function on $\mathbf{S} \times \mathbf{V}$ which has mean zero and is Lipschitz in $L_2(\mu)$ with respect to $\rho$. Let $V$ be a random variable defined on $(\mathbf{V}, \mathscr{V}, \mu)$, and let $\{V_i: i \geq 1\}$ be a sequence of independent copies of $V$. The limiting behavior of the process $S_n(s) = n^{-1/2}\sum^n_{i=1} f(s, V_i)$ is studied under an integrability condition on the metric entropy with bracketing in $L_2(\mu)$. This metric entropy condition is analogous to one which implies the continuity of the limiting Gaussian process. A tightness result is derived which, in conjunction with the results of Andersen and Dobric (1987), shows that a central limit theorem holds for the $S_n$-process. This result generalizes those of Dudley (1978), Dudley (1981) and Jain and Marcus (1975).