Annals of Statistics

Nonparametric regression with nonparametrically generated covariates

Enno Mammen, Christoph Rothe, and Melanie Schienle

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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.

Article information

Ann. Statist., Volume 40, Number 2 (2012), 1132-1170.

First available in Project Euclid: 18 July 2012

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression 62G20: Asymptotic properties

Nonparametric regression two-stage estimators simultaneous equation models empirical process


Mammen, Enno; Rothe, Christoph; Schienle, Melanie. Nonparametric regression with nonparametrically generated covariates. Ann. Statist. 40 (2012), no. 2, 1132--1170. doi:10.1214/12-AOS995.

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