Annals of Statistics

Some nonasymptotic results on resampling in high dimension, II: Multiple tests

Sylvain Arlot, Gilles Blanchard, and Etienne Roquain

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In the context of correlated multiple tests, we aim to nonasymptotically control the family-wise error rate (FWER) using resampling-type procedures. We observe repeated realizations of a Gaussian random vector in possibly high dimension and with an unknown covariance matrix, and consider the one- and two-sided multiple testing problem for the mean values of its coordinates. We address this problem by using the confidence regions developed in the companion paper [Ann. Statist. (2009), to appear], which lead directly to single-step procedures; these can then be improved using step-down algorithms, following an established general methodology laid down by Romano and Wolf [J. Amer. Statist. Assoc. 100 (2005) 94–108]. This gives rise to several different procedures, whose performances are compared using simulated data.

Article information

Ann. Statist., Volume 38, Number 1 (2010), 83-99.

First available in Project Euclid: 31 December 2009

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G10: Hypothesis testing
Secondary: 62G09: Resampling methods

Family-wise error multiple testing high-dimensional data nonasymptotic error control resampling resampled quantile


Arlot, Sylvain; Blanchard, Gilles; Roquain, Etienne. Some nonasymptotic results on resampling in high dimension, II: Multiple tests. Ann. Statist. 38 (2010), no. 1, 83--99. doi:10.1214/08-AOS668.

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