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Abstract Details
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Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 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 - #302892 |
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Title:
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Constrained Penalized Splines Regression in the Presence of Correlated Errors
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Author(s):
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Huan Wang*+ and Mary C. Meyer and Jean Opsomer
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Companies:
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Colorado State University and Colorado State University and Colorado State University
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Address:
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, , ,
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Keywords:
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shape constraints ;
penalized splines ;
AIC ;
correlated errors
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Abstract:
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We propose using penalized spines with shape constraints such as monotonicity or convexity for estimation and inference of regression functions in presence of correlated errors. Error structures include AR(p), MA(q), and ARMA(p,q). The parameters p and q as well as the penalty parameter are chosen by the AIC criterion. Simulations for comparing the behaviors of AIC, estimation of error structure and fit of the data for constrained penalized splines fit, unconstrained penalized splines fit and simple linear regression fit are conducted. The results show that our proposed estimation method behaves better than the other two methods in both estimating the structure of errors and mean function of the trend.
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