Oracally Efficient Two-Step Estimation of Generalized Additive Model
AbstractThe generalized additive model (GAM) is a multivariate nonparametric regression tool for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions and the constant, which are oracally efficient under weak dependence. The SBK technique is both computationally 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 the asymptotic theory. The method is applied to estimate insolvent probability and to obtain higher accuracy ratio than a previous study. Supplementary materials for this article are available online.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of the American Statistical Association.
Volume (Year): 108 (2013)
Issue (Month): 502 (June)
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Other versions of this item:
- Rong Liu & Lijian Yang & Wolfgang Karl Härdle, 2011. "Oracally Efficient Two-Step Estimation of Generalized Additive Model," SFB 649 Discussion Papers SFB649DP2011-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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