# An efficient algorithm for structured sparse quantile regression

## Author

Listed:
• Vahid Nassiri

()

• Ignace Loris

()

## 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

## Suggested Citation

• Vahid Nassiri & Ignace Loris, 2014. "An efficient algorithm for structured sparse quantile regression," Computational Statistics, Springer, vol. 29(5), pages 1321-1343, October.
• Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1321-1343
DOI: 10.1007/s00180-014-0494-1
as

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

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## References listed on IDEAS

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Full references (including those not matched with items on IDEAS)

### Keywords

Structured sparsity; Variable selection; Convex optimization;

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