Given the fact that technological advances have made it possible to record and store a large number of variables for experimental units, the problem of variable selection has been given more attention by researchers in recent years. Many studies have been carried out on the introduction of various methods for variable selection and the improvement of these methods. The objective of variable selection is to improve predictions, provide them faster and more costeffective, and make the models more interpretable. One type of data that has been given special attention in the fields of health, medicine, economic, and social and behavioral sciences is the longitudinal data. These type of data are produced by repeatedly collecting observations on experimental units over time. Longitudinal studies are important since they can determine the changes of response variable over time as well as the factors affecting these changes. Recently, the issue of variable selection in longitudinal studies has been received more attention.