Convex support vector regression
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
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.
Topics & Concepts
OverfittingRobust regressionOutlierSupport vector machineConvexityNonparametric regressionRegressionLeast squares support vector machineComputer scienceRegression analysisRegression diagnosticRobustness (evolution)Mathematical optimizationMathematicsArtificial intelligenceStatisticsMachine learningPolynomial regressionEconomicsBiochemistryChemistryArtificial neural networkFinancial economicsGeneStatistical Methods and InferenceAdvanced Statistical Methods and ModelsSparse and Compressive Sensing Techniques