The Annals of Statistics

Some nonasymptotic results on resampling in high dimension, I: Confidence regions

Sylvain Arlot, Gilles Blanchard, and Etienne Roquain

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We study generalized bootstrap confidence regions for the mean of a random vector whose coordinates have an unknown dependency structure. The random vector is supposed to be either Gaussian or to have a symmetric and bounded distribution. The dimensionality of the vector can possibly be much larger than the number of observations and we focus on a nonasymptotic control of the confidence level, following ideas inspired by recent results in learning theory. We consider two approaches, the first based on a concentration principle (valid for a large class of resampling weights) and the second on a resampled quantile, specifically using Rademacher weights. Several intermediate results established in the approach based on concentration principles are of interest in their own right. We also discuss the question of accuracy when using Monte Carlo approximations of the resampled quantities.

Article information

Ann. Statist. Volume 38, Number 1 (2010), 51-82.

First available in Project Euclid: 31 December 2009

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G15: Tolerance and confidence regions
Secondary: 62G09: Resampling methods

Confidence regions high-dimensional data nonasymptotic error control resampling cross-validation concentration inequalities resampled quantile


Arlot, Sylvain; Blanchard, Gilles; Roquain, Etienne. Some nonasymptotic results on resampling in high dimension, I: Confidence regions. Ann. Statist. 38 (2010), no. 1, 51--82. doi:10.1214/08-AOS667.

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