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.
Publié le : 1989-12-14
Classification:
Bootstrap,
regression,
asymptotic theory,
Edgeworth expansion,
Monte Carlo,
empirical distribution,
central limit theorem,
resampling,
62E20,
62J05
@article{1176347375,
author = {Navidi, William},
title = {Edgeworth Expansions for Bootstrapping Regression Models},
journal = {Ann. Statist.},
volume = {17},
number = {1},
year = {1989},
pages = { 1472-1478},
language = {en},
url = {http://dml.mathdoc.fr/item/1176347375}
}
Navidi, William. Edgeworth Expansions for Bootstrapping Regression Models. Ann. Statist., Tome 17 (1989) no. 1, pp. 1472-1478. http://gdmltest.u-ga.fr/item/1176347375/