Oracally Efficient Two-Step Estimation of Generalized Additive Model
AbstractGeneralized 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2011-016.
Length: 44 pages
Date of creation: Mar 2011
Date of revision:
Bandwidths; B spline; knots; link function; mixing; Nadaraya-Watson estimator;
Other versions of this item:
- Rong Liu & Lijian Yang & Wolfgang K. H�rdle, 2013. "Oracally Efficient Two-Step Estimation of Generalized Additive Model," Journal of the American Statistical Association, American Statistical Association, vol. 108(502), pages 619-631, June.
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