Oracally efficient two-step estimation of generalized additive model - Rong Liu; Lijian Yang; Wolfgang Karl Härdle (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2011)
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.
Keywords: 330; bandwidths; B spline; knots; link function; mixing; Nadaraya-Watson estimator; Multivariate Analyse; Nichtparametrisches Verfahren; Regression; Schätztheorie; Theorie