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Additive Interactive Regression Models: Circumvention of the Curse of Dimensionality

Published online by Cambridge University Press:  11 February 2009

Donald W.K. Andrews  and
Yoon-Jae Whang
Donald W.K. Andrews
Affiliation:
Yale University
Yoon-Jae Whang
Affiliation:
Yale University
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Abstract

This paper considers series estimators of additive interactive regression (AIR) models. AIR models are nonparametric regression models that generalize additive regression models by allowing interactions between different regressor variables. They place more restrictions on the regression function, however, than do fully nonparametric regression models. By doing so, they attempt to circumvent the curse of dimensionality that afflicts the estimation of fully non-parametric regression models.

In this paper, we present a finite sample bound and asymptotic rate of convergence results for the mean average squared error of series estimators that show that AIR models do circumvent the curse of dimensionality. A lower bound on the rate of convergence of these estimators is shown to depend on the order of the AIR model and the smoothness of the regression function, but not on the dimension of the regressor vector. Series estimators with fixed and data-dependent truncation parameters are considered.

Type
Articles
Information
Econometric Theory , Volume 6 , Issue 4 , December 1990 , pp. 466 - 479
Copyright
Copyright © Cambridge University Press 1990

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