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Convex Support Vector Regression
Julkaisun tekijät: Liao Zhiqiang, Dai Sheng, Kuosmanen Timo
Kustantaja: Cornell University
Julkaisuvuosi: 2022
Sarjan nimi: arxiv > stat
Numero sarjassa: 2209.12538
DOI: http://dx.doi.org/10.48550/arXiv.2209.12538
Verkko-osoite: https://arxiv.org/abs/2209.12538
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/176514770
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.
Ladattava julkaisu This is an electronic reprint of the original article. |