  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.
