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