TY - JOUR
T1 - Convex support vector regression
AU - Liao, Zhiqiang
AU - Dai, Sheng
AU - Kuosmanen, Timo
N1 - Funding Information:
The authors would like to thank the three anonymous reviewers for their helpful comments. We acknowledge the computational resources provided by the Aalto Science-IT project. Zhiqiang Liao gratefully acknowledges financial support from the Foundation for Economic Education (Liikesivistysrahasto) [grant no. 210038] and the Jenny and Antti Wihuri Foundation [grant no. 00220201]. Sheng Dai gratefully acknowledges financial support from the Foundation for Economic Education (Liikesivistysrahasto) [grant no. 220074] and the OP Group Research Foundation [grant no. 20230008].
Publisher Copyright:
© 2023 The Author(s)
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - Convex regression
KW - Overfitting
KW - Regularization
KW - Robustness and sensitivity analysis
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85160075894&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2023.05.009
DO - 10.1016/j.ejor.2023.05.009
M3 - Article
AN - SCOPUS:85160075894
SN - 0377-2217
VL - 313
SP - 858
EP - 870
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -