A generalized Newton algorithm for quantile regression models

Songfeng Zheng ()

Computational Statistics, 2014, vol. 29, issue 6, 1403-1426

Abstract: This paper formulates the quadratic penalty function for the dual problem of the linear programming associated with the $$L_1$$ L 1 constrained linear quantile regression model. We prove that the solution of the original linear programming can be obtained by minimizing the quadratic penalty function, with the formulas derived. The obtained quadratic penalty function has no constraint, thus could be minimized efficiently by a generalized Newton algorithm with Armijo step size. The resulting algorithm is easy to implement, without requiring any sophisticated optimization package other than a linear equation solver. The proposed approach can be generalized to the quantile regression model in reproducing kernel Hilbert space with slight modification. Extensive experiments on simulated data and real-world data show that, the proposed Newton quantile regression algorithms can achieve performance comparable to state-of-the-art. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Linear programming; Quadratic penalty function; Armijo step; $$L_1$$ L 1 constrained model (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-014-0498-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:29:y:2014:i:6:p:1403-1426

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-014-0498-x

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().