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Gaussian approximation of suprema of empirical processes

Authors: Victor Chernozhukov , Denis Chetverikov and Kengo Kato
Date: 01 August 2014
Type: Journal Article, Annals of Statistics, Vol. 42, No. 4, pp. 1564--1597
DOI: 10.1214/14-AOS1230

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.

Previous version:
Victor Chernozhukov, Denis Chetverikov and Kengo Kato August 2016, Gaussian approximation of suprema of empirical processes, cemmap Working Paper, CWP41/16, The IFS

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