Economics at your fingertips  

An SVM-like approach for expectile regression

Muhammad Farooq and Ingo Steinwart

Computational Statistics & Data Analysis, 2017, vol. 109, issue C, 159-181

Abstract: Expectile regression is an interesting tool for investigating conditional distributions beyond the conditional mean. It is well-known that expectiles can be described with the help of the asymmetric least square loss function, and this link makes it possible to estimate expectiles in a non-parametric framework with a support vector machine like approach. For the underlying optimization problem, an efficient sequential-minimal-optimization-based solver is developed and its convergence derived. The behavior of the solver is investigated by conducting various experiments, and the results are compared with the solver for quantile regression and the recent R-package ER-Boost.

Keywords: Asymmetric least square loss; Expectile regression; Support vector machines (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only.

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:

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2017-09-29
Handle: RePEc:eee:csdana:v:109:y:2017:i:c:p:159-181