Exploring Regression Structure Using Nonparametric Functional Estimation

Abstract

Average derivative functionals of regression are proposed for nonparametric model selection and diagnostics. The functionals are of the integral type, which under certain conditions allows their estimation at the usual parametric rate of n ^–1/2. We analyze asymptotic properties of the estimators of these functionals, based on kernel regression. These estimators can then be used for assessing the validity of various restrictions imposed on the form of regression. In particular, we show how they could be used to reduce the dimensionality of the model, assess the relative importance of predictors, measure the extent of nonlinearity and nonadditivity, and, under certain conditions, help identify projection directions in projection pursuit models and decide on the number of these directions.

Key Words:

• Kernel estimation
• Model selection
• Nonparametric functional estimation
• Nonparametric regression