The Annals of Statistics

How do bootstrap and permutation tests work?

Arnold Janssen and Thorsten Pauls

Full-text: Open access

Abstract

Resampling methods are frequently used in practice to adjust critical values of nonparametric tests. In the present paper a comprehensive and unified approach for the conditional and unconditional analysis of linear resampling statistics is presented. Under fairly mild assumptions we prove tightness and an asymptotic series representation for their weak accumulation points. From this series it becomes clear which part of the resampling statistic is responsible for asymptotic normality. The results leads to a discussion of the asymptotic correctness of resampling methods as well as their applications in testing hypotheses. They are conditionally correct iff a central limit theorem holds for the original test statistic. We prove unconditional correctness iff the central limit theorem holds or when symmetric random variables are resampled by a scheme of asymptotically random signs. Special cases are the m (n) out of k (n) bootstrap, the weighted bootstrap, the wild bootstrap and all kinds of permutation statistics. The program is carried out for convergent partial sums of rowwise independent infinitesimal triangular arrays in detail. These results are used to compare power functions of conditional resampling tests and their unconditional counterparts. The proof uses the method of random scores for permutation type statistics.

Article information

Source
Ann. Statist., Volume 31, Number 3 (2003), 768-806.

Dates
First available in Project Euclid: 25 June 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1056562462

Digital Object Identifier
doi:10.1214/aos/1056562462

Mathematical Reviews number (MathSciNet)
MR1994730

Zentralblatt MATH identifier
1028.62027

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

Keywords
Sample mean bootstrap weighted bootstrap wild bootstrap permutation statistics resampling tests conditional tests random scores rank statistics infinitely divisible laws two-sample test exchangeable variables

Citation

Janssen, Arnold; Pauls, Thorsten. How do bootstrap and permutation tests work?. Ann. Statist. 31 (2003), no. 3, 768--806. doi:10.1214/aos/1056562462. https://projecteuclid.org/euclid.aos/1056562462


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