Efficient Estimation Of Generalized Additive Nonparametric Regression Models
We define new procedures for estimating generalized additive nonparametric regression models that are more efficient than the Linton and H rdle (1996, Biometrika 83, 529 540) integration-based method and achieve certain oracle bounds. We consider criterion functions based on the Linear exponential family, which includes many important special cases. We also consider the extension to multiple parameter models like the gamma distribution and to models for conditional heteroskedasticity.
Volume (Year): 16 (2000)
Issue (Month): 04 (August)
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