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Abstract Details
Activity Number:
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636
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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Abstract - #303690 |
Title:
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Regularized Semiparametric Additive Mixed-Effects Models for High-Dimensional Longitudinal Data
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Author(s):
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Peter Song*+ and Yun Li and Naisyin Wang and Sijian Wang and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan and University of Wisconsin-Madison and University of Michigan
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Address:
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, , 48105,
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Keywords:
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longitudinal data ;
lasso ;
nonparametric regression ;
regularization ;
sparsity
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Abstract:
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In many biomedical studies some predictors are associated with disease outcomes in a nonlinear fashion. We consider a semi-parametric additive mixed-effects (SPAME) model for longitudinal studies that collect a large number of predictors. We proposed a new and effective regularization method in the SPAME model that assists us to detect and evaluate sparse signals. The novelty of our method is that it can determine automatically which predictors are unassociated, linearly associated, or nonlinearly associated with outcomes. We will illustrate our method on both simulation studies and real-world data analysis.
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Authors who are presenting talks have a * after their name.
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