  We review recent results for high-dimensional sparse linear regression in the
practical case of unknown variance. Different sparsity settings are covered,
including coordinate-sparsity, group-sparsity and variation-sparsity. The
emphasis is put on non-asymptotic analyses and feasible procedures. In
addition, a small numerical study compares the practical performance of three
schemes for tuning the Lasso estimator and some references are collected for
some more general models, including multivariate regression and nonparametric
regression.
