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arXiv · 1507.03887

An SVM-like Approach for Expectile Regression

Muhammad Farooq·Ingo Steinwart
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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.

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BibTeXRIS

Muhammad Farooq, Ingo Steinwart. 2015-07-14. An SVM-like Approach for Expectile Regression. https://arxiv.org/abs/1507.03887

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