An SVM-like Approach for Expectile Regression
Computation
2015-07-15 v1 Machine Learning
Abstract
Expectile regression is a nice 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 by a support vector machine like approach. In this work we develop an efficient sequential-minimal-optimization-based solver for the underlying optimization problem. The behavior of the solver is investigated by conducting various experiments and the results are compared with the recent R-package ER-Boost.
Cite
@article{arxiv.1507.03887,
title = {An SVM-like Approach for Expectile Regression},
author = {Muhammad Farooq and Ingo Steinwart},
journal= {arXiv preprint arXiv:1507.03887},
year = {2015}
}