The Annals of Statistics

Gaussian approximation of suprema of empirical processes

Victor Chernozhukov, Denis Chetverikov, and Kengo Kato

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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.

Article information

Source
Ann. Statist., Volume 42, Number 4 (2014), 1564-1597.

Dates
First available in Project Euclid: 7 August 2014

Permanent link to this document
https://projecteuclid.org/euclid.aos/1407420009

Digital Object Identifier
doi:10.1214/14-AOS1230

Mathematical Reviews number (MathSciNet)
MR3262461

Zentralblatt MATH identifier
1317.60038

Subjects
Primary: 60F17: Functional limit theorems; invariance principles 62E17: Approximations to distributions (nonasymptotic) 62G20: Asymptotic properties

Keywords
Coupling empirical process Gaussian approximation kernel estimation local empirical process series estimation supremum

Citation

Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo. Gaussian approximation of suprema of empirical processes. Ann. Statist. 42 (2014), no. 4, 1564--1597. doi:10.1214/14-AOS1230. https://projecteuclid.org/euclid.aos/1407420009


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Supplemental materials

  • Supplementary material: Supplement to “Gaussian approximation of suprema of empirical processes”. This supplemental file contains the additional technical proofs omitted in the main text, and some technical tools used in the proofs.