Spline-backfitted kernel smoothing of nonlinear additive autoregression model

Details Spline-backfitted kernel smoothing of nonlinear additive autoregression model Spline-backfitted kernel smoothing of nonlinear additive autoregression model

2006-12-22

arxiv.org/abs/math/0612677v5

Annals of Statistics 2007, Vol. 35, No. 6, 2474-2503

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/009053607000000488 the Annals of Statistics (http://www.imstat.org/aos/) by the Inst

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Spline-backfitted kernel smoothing of nonlinear additive autoregression model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Spline-backfitted kernel smoothing of nonlinear additive autoregression model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Spline-backfitted kernel smoothing of nonlinear additive autoregression model will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-531754