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Activity Number:
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24
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304464 |
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Title:
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Variable Selection in Partial Additive Mixed Models for Longitudinal Data
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Author(s):
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Daowen Zhang*+
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Companies:
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North Carolina State University
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Address:
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Department of Statistics, Raleigh, NC, 27695-8203,
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Keywords:
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Longitudinal data ; Mixed Models ; Smoothing ; Variable selection
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Abstract:
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In longitudinal studies with a potentially large number of covariates, investigators are often interested in identifying important variables that are predictive of the response. Suppose we can a priori divide the covariates into a parametric group and a nonparametric group. In this research, we propose a new method to simultaneously select important parametric covariate effects and nonparametric covariate effects in partial additive mixed models for longitudinal data. The proposed method treats the inverse of smoothing parameters as variance components in an induced working linear mixed model. The selection of fixed parameter effects and nonparametric effects is achieved by shrinking negligible fixed effects and induced variance components to zero. Simulation studies are conducted to evaluate the performance of the new method and a data analysis is used to illustrate its application
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