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Model Detection for Additive Models with Longitudinal Data

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In this paper, we consider the problem of variable selection and model detection in additive models with longitudinal data. Our approach is based on spline approximation for the components aided by two Smoothly Clipped Absolute Deviation (SCAD) penalty terms. It can perform model selection (finding both zero and linear components) and estimation simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and linear components selection. Besides, being theoretically justified, the proposed method is easy to understand and straightforward to implement. Extensive simulation studies as well as a real dataset are used to illustrate the performances.
Cite this paper
Wu, J. and Xue, L. (2014) Model Detection for Additive Models with Longitudinal Data. Open Journal of Statistics, 4, 868-878. doi: 10.4236/ojs.2014.410082.
 

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