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Activity Number:
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447
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301377 |
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Title:
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Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem
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Author(s):
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Aurore Delaigle*+ and Jianqing Fan and Raymond Carroll
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Companies:
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University of Bristol and Princeton University and Texas A&M University
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Address:
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, Bristol, BS8 1TW, United Kingdom
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Keywords:
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kernel ; smoothing ; measurement errors ; nonparametric regression ; bandwidth ; deconvolution
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Abstract:
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Local polynomial estimators are very popular techniques of nonparametric regression estimation and have received great attention in the literature. Their simplest version, the local constant estimator, can be easily extended to the errors-in-variables context by exploiting its similarity with the deconvolution kernel density estimator. The generalization of the higher order versions of the estimator, however, is not straightforward and has remained an open problem for the last 15 years. We propose local polynomial estimators of any order in the errors-in-variables context, discuss their asymptotic properties and illustrate their finite sample performance on numerical data examples.
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