TY - JOUR
T1 - Automatic variable selection for longitudinal generalized linear models
AU - Li, Gaorong
AU - Lian, Heng
AU - Feng, Sanying
AU - Zhu, Lixing
N1 - Funding Information:
Gaorong Li’s research was supported by the National Nature Science Foundation of China ( 11101014 ), the Specialized Research Fund for the Doctoral Program of Higher Education of China ( 20101103120016 ), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality ( PHR20110822 ), Training Programme Foundation for the Beijing Municipal Excellent Talents ( 2010D005015000002 ), the Fundamental Research Foundation of Beijing University of Technology ( X4006013201101 ) and Program for JingHua Talents in Beijing University of Technology. Heng Lian’s research was supported by Singapore MOE Tier 1 RG 36/09. Lixing Zhu’s research was supported by a grant from the Research Grants Council of Hong Kong , and a FRG grant from Hong Kong Baptist University, Hong Kong. The authors would like to thank the Editor, an associate editor, and the referees for their helpful comments that helped to improve an earlier version of this article. We are also grateful to Associate Professor Jianhui Zhou for providing the R code for the SCAD-GEE method.
PY - 2013/5
Y1 - 2013/5
N2 - 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.
AB - 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.
KW - Automatic variable selection
KW - Generalized estimating equations
KW - Generalized linear model
KW - Longitudinal data
KW - Oracle property
UR - http://www.scopus.com/inward/record.url?scp=84885018525&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2012.12.015
DO - 10.1016/j.csda.2012.12.015
M3 - Journal article
AN - SCOPUS:84885018525
SN - 0167-9473
VL - 61
SP - 174
EP - 186
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -