An overview on variable selection for longitudinal data

During the past two decades, there have been many new developments in longitudinal data analysis. Authors have made many efforts on devel- oping diverse models, along with inference procedures, for longitudinal data. More recently, researchers in longitudinal modeling have begun ad- dressing the vital issue of variable selection. Model selection criteria such as AIC, BIC, C_p, LASSO and SCAD can be extended to longitudinal data, although care is required to adapt the classical ideas and formulas to deal with within-subject correlation. This chapter presents a review on recent developments on variable selection criteria for longitudinal data.