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Penalized generalized estimating equations with Elastic Net or L2-SmoothlyClipped Absolute Deviation penalization are proposed to simultaneously selectthe most important variables and estimate their effects for longitudinalGaussian data when multicollinearity is present. The method is able toconsistently select and estimate the main effects even when strong correlationsare present. In addition, the potential pitfall of time-dependent covariates isclarified. Both asymptotic theory and simulation results reveal theeffectiveness of penalization as a data mining tool for longitudinal data,especially when a large number of variables is present. The method isillustrated by mining for the main determinants of life expectancy in Europe.

Data Mining for Longitudinal Data under Multicollinearity and Time Dependence using Penalized Generalized Estimating Equations