Application of nonparametric and semiparametric regression techniques to high-dimensional time series data has been hampered due to the lack of effective tools to address the “curse of dimensionality.” Under rather weak conditions, we propose spline-backfitted kernel estimators of the component functions for the nonlinear additive time series data that are both computationally expedient so they are usable for analyzing very high-dimensional time series, and theoretically reliable so inference can be made on the component functions with confidence. Simulation experiments have provided strong evidence that corroborates the asymptotic theory.
Publié le : 2007-12-15
Classification:
Bandwidths,
B spline,
knots,
local linear estimator,
mixing,
Nadaraya–Watson estimator,
nonparametric regression,
62M10,
62G08
@article{1201012969,
author = {Wang, Li and Yang, Lijian},
title = {Spline-backfitted kernel smoothing of nonlinear additive autoregression model},
journal = {Ann. Statist.},
volume = {35},
number = {1},
year = {2007},
pages = { 2474-2503},
language = {en},
url = {http://dml.mathdoc.fr/item/1201012969}
}
Wang, Li; Yang, Lijian. Spline-backfitted kernel smoothing of nonlinear additive autoregression model. Ann. Statist., Tome 35 (2007) no. 1, pp. 2474-2503. http://gdmltest.u-ga.fr/item/1201012969/