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EFFICIENT ESTIMATION OF GENERALIZED ADDITIVE NONPARAMETRIC REGRESSION MODELS

Published online by Cambridge University Press:  01 August 2000

Oliver B. Linton
Affiliation:
London School of Economics and Yale University

Abstract

We define new procedures for estimating generalized additive nonparametric regression models that are more efficient than the Linton and Härdle (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.

Type
Research Article
Copyright
© 2000 Cambridge University Press

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