Composite support vector quantile regression estimation

Jooyong Shim (), Changha Hwang () and Kyungha Seok ()

Computational Statistics, 2014, vol. 29, issue 6, 1665 pages

Abstract: In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the CSVQR utilizing the iteratively reweighted least squares (IRWLS) procedure is proposed to overcome weakness of the QP based method in terms of computation time. The IRWLS procedure based method enables us to derive a generalized cross validation (GCV) function that is easier and faster than the conventional cross validation function. The GCV function facilitates choosing the hyperparameters that affect the performance of the CSVQR and saving computation time. Numerical experiment results are presented to illustrate the performance of the proposed method Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Composite quantile regression; Generalized cross validation; Iteratively reweighted least squares procedure; Model selection; Quadratic programming; Quantile regression; Support vector regression (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/s00180-014-0511-4 (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:1651-1665

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

DOI: 10.1007/s00180-014-0511-4

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 ().