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An efficient algorithm for structured sparse quantile regression

Listed author(s):
  • Vahid Nassiri

    ()

  • Ignace Loris

    ()

Registered author(s):

    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

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    File URL: http://hdl.handle.net/10.1007/s00180-014-0494-1
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    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 29 (2014)
    Issue (Month): 5 (October)
    Pages: 1321-1343

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    Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1321-1343
    DOI: 10.1007/s00180-014-0494-1
    Contact details of provider: Web page: http://www.springer.com

    Order Information: Web: http://www.springer.com/statistics/journal/180/PS2

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