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