Journal Article

GAUSSIAN APPROXIMATIONS AND MULTIPLIER BOOTSTRAP FOR MAXIMA OF SUMS OF HIGH-DIMENSIONAL RANDOM VECTORS

Kato, Kengo, Chetverikov, Denis, Chernozhukov, Victor

2013

Overview

We derive a Gaussian approximation result for the maximum of a sum of high-dimensional random vectors. Specifically, we establish conditions under which the distribution of the maximum is approximated by that of the maximum of a sum of the Gaussian random vectors with the same covariance matrices as the original vectors. This result applies when the dimension of random vectors (p) is large compared to the sample size (n); in fact, p can be much larger than n, without restricting correlations of the coordinates of these vectors. We also show that the distribution of the maximum of a sum of the random vectors with unknown covariance matrices can be consistently estimated by the distribution of the maximum of a sum of the conditional Gaussian random vectors obtained by multiplying the original vectors with i.i.d. Gaussian multipliers. This is the Gaussian multiplier (or wild) bootstrap procedure. Here too, p can be large or even much larger than n. These distributional approximations, either Gaussian or conditional Gaussian, yield a high-quality approximation to the distribution of the original maximum, often with approximation error decreasing polynomially in the sample size, and hence are of interest in many applications. We demonstrate how our Gaussian approximations and the multiplier bootstrap can be used for modern highdimensional estimation, multiple hypothesis testing, and adaptive specification testing. All these results contain nonasymptotic bounds on approximation errors.

• Publisher: Institute of Mathematical Statistics,The Institute of Mathematical Statistics
• Subject: 62E17 / 62F40 / anti-concentration / Approximation / Bootstrap method / Covariance matrices / Dantzig selector / Error rates / Estimators / high dimensionality / Hypothesis testing / Inference / Mathematical maxima / Mathematical vectors / maximum of vector sums / Non Gaussianity / Normal distribution / Null hypothesis / Random variables / Sample size / Slepian / Stein method / Studies