We analyse methods based on the block bootstrap and leave-out cross-validation, for choosing the bandwidth in nonparametric regression when errors have an almost arbitrarily long range of dependence. A novel analytical device for modelling the dependence structure of errors is introduced. This allows a concise theoretical description of the way in which the range of dependence affects optimal bandwidth choice. It is shown that, provided block length or leave-out number, respectively, are chosen appropriately, both techniques produce first-order optimal bandwidths. Nevertheless, the block bootstrap has far better empirical properties, particularly under long-range
dependence.
Publié le : 1995-12-14
Classification:
Bandwidth choice,
block bootstrap,
correlated errors,
cross-validation,
curve estimation,
kernel estimator,
local linear smoothing,
long-range dependence,
mean squared error,
nonparametric regression,
resampling,
short-range dependence,
62G07,
62G09,
62M10
@article{1034713640,
author = {Hall, Peter and Lahiri, Soumendra Nath and Polzehl, J\"org},
title = {On bandwidth choice in nonparametric regression with both short- and long-range dependent errors},
journal = {Ann. Statist.},
volume = {23},
number = {6},
year = {1995},
pages = { 1921-1936},
language = {en},
url = {http://dml.mathdoc.fr/item/1034713640}
}
Hall, Peter; Lahiri, Soumendra Nath; Polzehl, Jörg. On bandwidth choice in nonparametric regression with both short- and long-range dependent errors. Ann. Statist., Tome 23 (1995) no. 6, pp. 1921-1936. http://gdmltest.u-ga.fr/item/1034713640/