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Abstract Details

Activity Number: 636
Type: Invited
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #303690
Title: Regularized Semiparametric Additive Mixed-Effects Models for High-Dimensional Longitudinal Data
Author(s): Peter Song*+ and Yun Li and Naisyin Wang and Sijian Wang and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan and University of Wisconsin-Madison and University of Michigan
Address: , , 48105,
Keywords: longitudinal data ; lasso ; nonparametric regression ; regularization ; sparsity

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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