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Abstract Details
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Activity Number:
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575
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 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 - #301694 |
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Title:
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Local Polynomial Regression and Mixed Models
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Author(s):
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Li-Shan Huang*+ and Kung-Sik Chan
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Companies:
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National Tsing Hua University and University of Iowa
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Address:
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Institute of Statistics, HsinChu, 300, TAIWAN
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Keywords:
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Trigonometric basis functions ;
projection ;
ANOVA decomposition
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Abstract:
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Penalized spline smoothing has been shown to connect to linear mixed models in statistical literature. In this paper, we show that local polynomial regression when expressed in a projection framework also has interesting connections to mixed models. Under some conditions, the local polynomial projection approach admits an equivalent mixed model formulation where the fixed-effects part includes the polynomial basis functions. Then we further show that asymptotically the trigonometric functions are the penalized basis in the mixed model formulation. These results suggest a new smoothing approach using a combination of unpenalized polynomials and penalized trigonometric functions. We illustrate the potential usefulness of the new approach with real data analysis.
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