The nature of statistical learning theory
The nature of statistical learning theory
A tutorial on support vector regression
Statistics and Computing
Primal-Dual Monotone Kernel Regression
Neural Processing Letters
All of Nonparametric Statistics (Springer Texts in Statistics)
All of Nonparametric Statistics (Springer Texts in Statistics)
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Archimedean copula estimation using Bayesian splines smoothing techniques
Computational Statistics & Data Analysis
Incorporating prior knowledge in support vector regression
Machine Learning
GeD spline estimation of multivariate Archimedean copulas
Computational Statistics & Data Analysis
Linear B-spline copulas with applications to nonparametric estimation of copulas
Computational Statistics & Data Analysis
Thresholding methods to estimate copula density
Journal of Multivariate Analysis
Cutting plane method for continuously constrained kernel-based regression
IEEE Transactions on Neural Networks
Semiparametric bivariate Archimedean copulas
Computational Statistics & Data Analysis
On the axiomatization of some classes of discrete universal integrals
Knowledge-Based Systems
Multivariate convex support vector regression with semidefinite programming
Knowledge-Based Systems
A weighted twin support vector regression
Knowledge-Based Systems
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Copula has become a standard tool in describing dependent relations between random variables. This paper proposes a nonparametric bivariate copula estimation method based on shape-restricted @e-support vector regression (@e-SVR). This method explicitly supplements the classical @e-SVR with constraints related to three shape restrictions: grounded, marginal and 2-increasing, which are the necessary and sufficient conditions for a bivariate function to be a copula. This nonparametric method can be reformulated to a convex quadratic programming, which is computationally tractable. Experiments on both five artificial data sets and three international stock indexes clearly showed that it could achieve significantly better performance than common parametric models and kernel smoother.