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 OJS  Vol.4 No.3 , April 2014
Automatic Variable Selection for Single-Index Random Effects Models with Longitudinal Data
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.
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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