This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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499
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 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 - #306498 |
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Title:
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Spline-Backfitted Kernel Smoothing of Generalized Additive Model
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Author(s):
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Rong Liu*+ and Lijian Yang
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Companies:
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University of Toledo and Michigan State University
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Address:
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, Toledo, OH, 43606, USA
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Keywords:
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Bandwidths ;
B spline ;
kernel ;
knots ;
link function ;
mixing
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Abstract:
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Generalized additive models (GAM) have been popular in multivariate nonparametric regression with non-Gaussian responses including binary and count data. We propose spline-backfitted kernel (SBK) estimator for the GAM time series data with oracle efficiency. It is both computational expedient and theoretically reliable, thus usable for analyzing very high-dimensional time series and inference can be made on component functions with confidence. Simulation evidence strongly corroborates with the asymptotic theory.
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Authors who are presenting talks have a * after their name.
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