Nonparametric regression with nonparametrically generated covariates
Enno Mammen, Christoph Rothe, and Melanie Schienle
Source: Ann. Statist. Volume 40, Number 2
(2012), 1132-1170.
Abstract
We analyze the statistical properties of nonparametric regression estimators using covariates which are not directly observable, but have be estimated from data in a preliminary step. These so-called generated covariates appear in numerous applications, including two-stage nonparametric regression, estimation of simultaneous equation models or censored regression models. Yet so far there seems to be no general theory for their impact on the final estimator’s statistical properties. Our paper provides such results. We derive a stochastic expansion that characterizes the influence of the generation step on the final estimator, and use it to derive rates of consistency and asymptotic distributions accounting for the presence of generated covariates.
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Keywords: Nonparametric regression; two-stage estimators; simultaneous equation models; empirical process
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1342625464
Digital Object Identifier: doi:10.1214/12-AOS995
Zentralblatt MATH identifier: 06073788
Mathematical Reviews number (MathSciNet): MR2985946
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