Convex support vector regression

Zhiqiang Liao, Sheng Dai and Timo Kuosmanen

European Journal of Operational Research, 2024, vol. 313, issue 3, 858-870

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.

Keywords: Robustness and sensitivity analysis; Convex regression; Support vector regression; Overfitting; Regularization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:313:y:2024:i:3:p:858-870

DOI: 10.1016/j.ejor.2023.05.009

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