Convex Support Vector Regression
Methodology
2022-09-27 v1
Abstract
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression (CSVR) method, which effectively combines the key elements of convex regression and support vector regression. Numerical experiments demonstrate the performance of CSVR in prediction accuracy and robustness that compares favorably with other state-of-the-art methods.
Cite
@article{arxiv.2209.12538,
title = {Convex Support Vector Regression},
author = {Zhiqiang Liao and Sheng Dai and Timo Kuosmanen},
journal= {arXiv preprint arXiv:2209.12538},
year = {2022}
}