# An efficient algorithm for structured sparse quantile regression

## Author Info

Listed author(s):
• Vahid Nassiri

()

• Ignace Loris

()

Registered author(s):

## 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

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File URL: http://hdl.handle.net/10.1007/s00180-014-0494-1

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## Bibliographic Info

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

## References

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