Model selection using a penalized data-splitting device is studied in the context of nonparametric regression. Finite sample bounds under mild conditions are obtained. The resulting estimates are adaptive for large classes of functions.
Publié le : 2003-02-14
Classification:
Adaptive estimation,
classification,
data-splitting,
least squares estimation,
model selection,
penalized least squares,
VC-major classes,
60F05,
60F17,
60G15,
62E20
@article{1046294464,
author = {Wegkamp, Marten},
title = {Model selection in nonparametric regression},
journal = {Ann. Statist.},
volume = {31},
number = {1},
year = {2003},
pages = { 252-273},
language = {en},
url = {http://dml.mathdoc.fr/item/1046294464}
}
Wegkamp, Marten. Model selection in nonparametric regression. Ann. Statist., Tome 31 (2003) no. 1, pp. 252-273. http://gdmltest.u-ga.fr/item/1046294464/