Automatic variable selection for longitudinal generalized linear models

Gaorong Li*, Heng Lian, Sanying Feng, Lixing Zhu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)174-186
Number of pages13
JournalComputational Statistics and Data Analysis
Publication statusPublished - May 2013

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Automatic variable selection
  • Generalized estimating equations
  • Generalized linear model
  • Longitudinal data
  • Oracle property


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