scholarly journals Asymptotically efficient estimates for nonparametric regression models

2006 ◽  
Vol 76 (8) ◽  
pp. 852-860 ◽  
Author(s):  
L. Galtchouk ◽  
S. Pergamenshchikov
Keyword(s):  
Nonparametric Regression ◽  
Regression Models ◽  
Asymptotically Efficient

Efficient estimation for error component seemingly unrelated nonparametric regression models

Metrika ◽  
2009 ◽  
Vol 73 (1) ◽  
pp. 121-138 ◽  
Author(s):  
Bin Zhou ◽  
Qinfeng Xu ◽  
Jinhong You
Keyword(s):  
Nonparametric Regression ◽  
Regression Models ◽  
Error Component ◽  
Efficient Estimation

scholarly journals Additive Interactive Regression Models: Circumvention of the Curse of Dimensionality

1990 ◽  
Vol 6 (4) ◽  
pp. 466-479 ◽  
Author(s):  
Donald W.K. Andrews ◽  
Yoon-Jae Whang
Keyword(s):  
Rate Of Convergence ◽  
Nonparametric Regression ◽  
Regression Models ◽  
Regression Function ◽  
Curse Of Dimensionality ◽  
Finite Sample ◽  
Squared Error ◽  
Convergence Results ◽  
Additive Regression ◽  
Parametric Regression Models

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.

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