An SVM-like Approach for Expectile Regression
This work provides an incremental improvement for researchers and practitioners using expectile regression in statistical modeling.
The authors tackled the problem of efficiently solving expectile regression by developing a sequential-minimal-optimization-based solver, achieving competitive performance compared to the ER-Boost R-package in experiments.
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.