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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 - #301006 |
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Title:
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Local Polynomial Composite Quantile Regression
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Author(s):
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Bo Kai*+ and Runze Li and Hui Zou
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Companies:
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The Pennsylvania State University and The Pennsylvania State University and The University of Minnesota
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Address:
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326 Thomas Building, University Park, PA, 16802,
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Keywords:
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nonparametric estimation ; local polynomial regression ; composite quantile regression ; asymptotic efficiency
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Abstract:
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Nonparametric regression is a useful statistical tool to explore fine features in the data, and has been applied in various disciplines. In this talk, we propose local polynomial composite quantile regression for nonparametric regression models. We derive the asymptotic bias, variance and normality of the proposed estimate. Asymptotic relative efficiency of the proposed estimate to the local polynomial regression under the least squares loss is investigated. We show that the proposed estimate can be much more efficient than the local polynomial regression estimate with the squared loss for various non-normal errors, and is almost as efficient as the LS estimate for normal error. Simulation is conducted to examine the performance of the proposed estimates. The simulation results are consistent with our theoretic findings. A real data example is used to illustrate the proposed procedures.
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