  We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent
for high-dimensional model selection in linear and Gaussian graphical models.
Our conditions for consistency cover more general situations than those
accomplished in previous work: we prove that restricted eigenvalue conditions
(Bickel et al., 2008) are also sufficient for sparse structure estimation.
