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Spline-backfitted kernel smoothing of nonlinear additive autoregression model
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.Comment: 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
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