Open Access
December 2007 Spline-backfitted kernel smoothing of nonlinear additive autoregression model
Li Wang, Lijian Yang
Ann. Statist. 35(6): 2474-2503 (December 2007). DOI: 10.1214/009053607000000488


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.


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Li Wang. Lijian Yang. "Spline-backfitted kernel smoothing of nonlinear additive autoregression model." Ann. Statist. 35 (6) 2474 - 2503, December 2007.


Published: December 2007
First available in Project Euclid: 22 January 2008

zbMATH: 1129.62038
MathSciNet: MR2382655
Digital Object Identifier: 10.1214/009053607000000488

Primary: 62M10
Secondary: 62G08

Keywords: B spline , Bandwidths , knots , local linear estimator , Mixing , Nadaraya–Watson estimator , Nonparametric regression

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.35 • No. 6 • December 2007
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