Spline-backfitted kernel smoothing of nonlinear additive autoregression model
Li Wang and Lijian Yang
Source: Ann. Statist. Volume 35, Number 6 (2007), 2474-2503.
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.
Primary Subjects: 62M10
Secondary Subjects: 62G08
Keywords: Bandwidths; B spline; knots; local linear estimator; mixing; Nadaraya–Watson estimator; nonparametric regression
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1201012969
Digital Object Identifier: doi:10.1214/009053607000000488
Mathematical Reviews number (MathSciNet):
MR2382655
Zentralblatt MATH identifier:
1129.62038
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