We use L\'evy processes to generate joint prior distributions for a location
parameter $\bbeta = (\beta_1,...,\beta_p) $ as $p$ grows large. This leads to
the class of local-global shrinkage rules. We extend this framework to
large-scale regularized regression for $p>n$ problems, and provide thorough
comparisons with current methodologies.