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Automatic Variable Selection for Single-Index Random Effects Models with Longitudinal Data

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DOI: 10.4236/ojs.2014.43022    3,160 Downloads   4,626 Views  

ABSTRACT

We consider the problem of variable selection for the single-index random effects models with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold. The proposed method shares some of the desired features of existing variable selection methods: the resulting estimator enjoys the oracle property; the proposed procedure avoids the convex optimization problem and is flexible and easy to implement. Moreover, we use the penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of our method, and a real dataset is analyzed for further illustration.


Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Yang, S. and Xue, L. (2014) Automatic Variable Selection for Single-Index Random Effects Models with Longitudinal Data. Open Journal of Statistics, 4, 230-237. doi: 10.4236/ojs.2014.43022.

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