For consistency (even oracle properties) of estimation and model prediction,
almost all existing methods of variable/feature selection critically depend on
sparsity of models. However, for ``large $p$ and small $n$" models sparsity
assumption is hard to check and particularly, when this assumption is violated,
the consistency of all existing estimations is usually impossible because
working models selected by existing methods such as the LASSO and the Dantzig
selector are usually biased. To attack this problem, we in this paper propose
adaptive post-Dantzig estimation and model prediction.