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Activity Number: 329
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318343
Title: Nonparametric Covariate-Adjusted Regression
Author(s): Aurore Delaigle* and Wenxin Zhou and Peter Hall
Companies: University of Melbourne and Princeton and University of Melbourne
Keywords: errors-in-variables ; smoothing ; multiplicative errors

We consider nonparametric estimation of a regression curve when the data are observed with multiplicative distortion which depends on an observed confounding variable. We suggest several estimators and show that, although our nonparametric estimators are constructed from predictors of the unobserved undistorted data, they have the same first order asymptotic properties as the standard estimators that could be computed if the undistorted data were available. We illustrate the numerical performance of our methods on simulated and real data sets.

Authors who are presenting talks have a * after their name.

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