Variable selection in generalized estimating equations via empirical likelihood and Gaussian pseudo-likelihood

AIC and BIC based on either empirical likelihood (EAIC and EBIC) or Gaussian pseudo-likelihood (GAIC and GBIC) are proposed to select variables in longitudinal data analysis. Their performances are evaluated in the framework of the generalized estimating equations via intensive simulation studies. Our findings are: (i) GAIC and GBIC outperform other existing methods in selecting variables; (ii) EAIC and EBIC are effective in selecting covariates only when the working correlation structure is correctly specified; (iii) GAIC and GBIC perform well regardless the working correlation structure is correctly specified or not. A real dataset is also provided to illustrate the findings.