Gaussian approximation of suprema of empirical processes

TL;DR

This paper introduces a novel nonasymptotic Gaussian approximation method for the supremum of empirical processes, applicable to complex, unbounded, and diverging entropy function classes, with broad statistical applications.

Contribution

It presents a new direct approach to Gaussian approximation of empirical process suprema, avoiding full process approximation and handling unbounded, diverging entropy classes.

Findings

Provides a nonasymptotic approximation bound.

Applicable to non-Donsker classes with diverging entropy.

Enables Gaussian approximation in nonparametric estimation contexts.

Abstract

This paper develops a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical processes in the sup-norm. We prove an abstract approximation theorem applicable to a wide variety of statistical problems, such as construction of uniform confidence bands for functions. Notably, the bound in the main approximation theorem is nonasymptotic and the theorem allows for functions that index the empirical process to be unbounded and have entropy divergent with the sample size. The proof of the approximation theorem builds on a new coupling inequality for maxima of sums of random vectors, the proof of which depends on an effective use of Stein's method for normal approximation, and some new empirical process techniques. We study applications of this approximation theorem to local and series empirical processes arising in nonparametric estimation via kernel and series methods, where the classes of functions change with the sample size and are non-Donsker. Importantly, our new technique is able to prove the Gaussian approximation for the supremum type statistics under weak regularity conditions, especially concerning the bandwidth and the number of series functions, in those examples.