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Abstract Details
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Activity Number:
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353
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 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 - #306319 |
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Title:
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Spline-Backfitted Kernel Estimation for Functional-Coefficient Autoregressive Models
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Author(s):
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Joshua Patrick*+ and Jane Harvill
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Companies:
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Baylor University and Baylor University
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Address:
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1825 S. 5th St., Waco, TX, 76706, United States
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Keywords:
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oracle smoother ;
FCAR models ;
nonlinear time series ;
spline-backfitted Kernel estimation
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Abstract:
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The functional-coefficient autoregressive model is a useful structure for reducing the size of the class of nonlinear times series models. Local linear regression has been shown to be an effective technique for estimating the coefficient functions of these models. We propose an oracle estimation method that uses a spline-backfitted kernel (SBK) approach for estimating the coefficient functions. The SBK estimators are computationally expedient and asymptotically equivalent to univariate Nadaraya-Watson kernel estimators. The performance of the SBK approach is compared to the local linear regression approach through simulations and a real world example.
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