  Quantile regression is studied in combination with a penalty which promotes
structured (or group) sparsity. A mixed $\ell_{1,\infty}$-norm on the parameter
vector is used to impose structured sparsity on the traditional quantile
regression problem. An algorithm is derived to calculate the piece-wise linear
solution path of the corresponding minimization problem. A Matlab
implementation of the proposed algorithm is provided and some applications of
the methods are also studied.
