Department of Mathematics
Automatic variable selection for longitudinal generalized linear models
We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE. 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 propose a penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of SGEE, and a real dataset is analyzed for further illustration. © 2012 Elsevier B.V. All rights reserved.
Automatic variable selection, Generalized estimating equations, Generalized linear model, Longitudinal data, Oracle property
Source Publication Title
Computational Statistics & Data Analysis
Link to Publisher's Edition
Li, G., Lian, H., Feng, S., & Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis, 61, 174-186. https://doi.org/10.1016/j.csda.2012.12.015