Regressions with Berkson errors in covariates - A nonparametric approach
AbstractThis paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via Monte Carlo simulations as well as through an epidemiological application investigating the effect of particulate air pollution on respiratory health. These examples illustrate that Berkson errors can clearly not be neglected in nonlinear regression models and that the proposed method represents an effective remedy.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1308.2836.
Date of creation: Aug 2013
Date of revision:
Publication status: Published in Annals of Statistics 2013, Vol. 41, No. 3, 1642-1668
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Web page: http://arxiv.org/
Other versions of this item:
- Susanne Schennach, 2013. "Regressions with Berkson errors in covariates- a nonparametric approach," CeMMAP working papers CWP22/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- NEP-ALL-2013-08-23 (All new papers)
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