An efficient algorithm for structured sparse quantile regression

作者：Nassiri Vahid; Loris Ignace^*

来源：Computational Statistics, 2014, 29(5): 1321-1343.

DOI：10.1007/s00180-014-0494-1

• Structured sparsity
• Variable selection
• Convex optimization

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 -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.

• 出版日期2014-10