  We study how correlations in the design matrix influence Lasso prediction.
First, we argue that the higher the correlations are, the smaller the optimal
tuning parameter is. This implies in particular that the standard tuning
parameters, that do not depend on the design matrix, are not favorable.
Furthermore, we argue that Lasso prediction works well for any degree of
correlations if suitable tuning parameters are chosen. We study these two
subjects theoretically as well as with simulations.
