The Annals of Statistics

Edgeworth Expansions for Bootstrapping Regression Models

William Navidi

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Abstract

The asymptotic performance of the bootstrap in linear regression models is studied. Edgeworth expansions show that asymptotically, the bootstrap is always at least as good as, and in some cases better than, the classical normal approximation. The performances of both the bootstrap and the normal approximation depend on the rate of increase in the elements of the design matrix.

Article information

Source
Ann. Statist., Volume 17, Number 4 (1989), 1472-1478.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176347375

Digital Object Identifier
doi:10.1214/aos/1176347375

Mathematical Reviews number (MathSciNet)
MR1026293

Zentralblatt MATH identifier
0694.62011

JSTOR
links.jstor.org

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62J05: Linear regression

Keywords
Bootstrap regression asymptotic theory Edgeworth expansion Monte Carlo empirical distribution central limit theorem resampling

Citation

Navidi, William. Edgeworth Expansions for Bootstrapping Regression Models. Ann. Statist. 17 (1989), no. 4, 1472--1478. doi:10.1214/aos/1176347375. https://projecteuclid.org/euclid.aos/1176347375


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