A Sharp Lower-tail Bound for Gaussian Maxima with Application to Bootstrap Methods in High Dimensions.

Although there is an extensive literature on the maxima of Gaussian processes, there are relatively few non-asymptotic bounds on their lower-tail probabilities. The aim of this paper is to develop such a bound, while also allowing for many types of dependence. Let $(\xi_1,\dots,\xi_N)$ be a centered Gaussian vector with standardized entries, whose correlation matrix $R$ satisfies $\max_{i\neq j} R_{ij}\leq \rho_0$ for some constant $\rho_0\in (0,1)$. Then, for any $\epsilon_0\in(0,\sqrt{1-\rho_0})$, we establish an upper bound on the probability $\mathbb{P}(\max_{1\leq j\leq N} \xi_j\leq \epsilon_0\sqrt{2\log(N)})$ in terms of $(\rho_0,\epsilon_0,N)$. The bound is also sharp, in the sense that it is attained up to a constant, independent of $N$. Next, we apply this result in the context of high-dimensional statistics, where we simplify and weaken conditions that have recently been used to establish near-parametric rates of bootstrap approximation. Lastly, an interesting aspect of this application is that it makes use of recent refinements of Bourgain and Tzafriri's "restricted invertibility principle".