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May 2003 The Impact of the Bootstrap on Statistical Algorithms and Theory
Rudolf Beran
Statist. Sci. 18(2): 175-184 (May 2003). DOI: 10.1214/ss/1063994972

Abstract

Bootstrap ideas yield remarkably effective algorithms for realizing certain programs in statistics. These include the construction of (possibly simultaneous) confidences sets and tests in classical models for which exact or asymptotic distribution theory is intractable. Success of the bootstrap, in the sense of doing what is expected under a probability model for data, is not universal. Modifications to Efron's definition of the bootstrap are needed to make the idea work for modern procedures that are not classically regular.

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Rudolf Beran. "The Impact of the Bootstrap on Statistical Algorithms and Theory." Statist. Sci. 18 (2) 175 - 184, May 2003. https://doi.org/10.1214/ss/1063994972

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Published: May 2003
First available in Project Euclid: 19 September 2003

zbMATH: 1331.62177
MathSciNet: MR2026078
Digital Object Identifier: 10.1214/ss/1063994972

Rights: Copyright © 2003 Institute of Mathematical Statistics

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Vol.18 • No. 2 • May 2003
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