Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56626
Authors: 
Liu, Rong
Yang, Lijian
Härdle, Wolfgang Karl
Year of Publication: 
2011
Series/Report no.: 
SFB 649 discussion paper 2011-016
Abstract: 
Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency. The SBK techniques is both computational expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates with the asymptotic theory.
Subjects: 
bandwidths
B spline
knots
link function
mixing
Nadaraya-Watson estimator
Document Type: 
Working Paper

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