Spline-backfitted kernel smoothing of nonlinear additive autoregression model
Abstract
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.
Cite
@article{arxiv.math/0612677,
title = {Spline-backfitted kernel smoothing of nonlinear additive autoregression model},
author = {Li Wang and Lijian Yang},
journal= {arXiv preprint arXiv:math/0612677},
year = {2009}
}
Comments
Published in at http://dx.doi.org/10.1214/009053607000000488 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)