Model Detection for Additive Models with Longitudinal Data

Jian Wu; Liugen Xue

doi:10.4236/ojs.2014.410082

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Open Journal of Statistics > Vol.4 No.10, December 2014

Model Detection for Additive Models with Longitudinal Data ()

¹College of Applied Sciences, Beijing University of Technology, Beijing, China.
²College of Science, Northeastern University, Shenyang, China.

DOI: 10.4236/ojs.2014.410082 PDF HTML XML 3,091 Downloads 3,564 Views Citations

Abstract

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.

Keywords

Additive Model, Model Detection, Variable Selection, SCAD Penalty

Share and Cite:

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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