An efficient algorithm for structured sparse quantile regression |
| |
Authors: | Vahid Nassiri Ignace Loris |
| |
Institution: | 1. Department of Mathematics, Vrije Universiteit Brussel, Brussels, Belgium 2. Department of Mathematics, Université Libre de Bruxelles, Brussels, Belgium
|
| |
Abstract: | An efficient algorithm is derived for solving the quantile regression problem combined with a group sparsity promoting penalty. The group sparsity of the regression parameters is achieved by using a \(\ell _{1,\infty }\) -norm penalty (or constraint) on the regression parameters. The algorithm is efficient in the sense that it obtains the regression parameters for a wide range of penalty parameters, thus enabling easy application of a model selection criteria afterwards. A Matlab implementation of the proposed algorithm is provided and some applications of the methods are studied. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|