An efficient algorithm for structured sparse quantile regression

Vahid Nassiri () and Ignace Loris ()

Computational Statistics, 2014, vol. 29, issue 5, 1343 pages

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 $$\ell _{1,\infty }$$ ℓ 1 , ∞ -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. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Structured sparsity; Variable selection; Convex optimization (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:29:y:2014:i:5:p:1321-1343

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DOI: 10.1007/s00180-014-0494-1

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