Machine Learning
Kernel principal component analysis
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Archimedean copula estimation using Bayesian splines smoothing techniques
Computational Statistics & Data Analysis
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
| Hi-index | 0.00 |
Copula has become the standard tool in dependence modeling. With the aide of copula, the estimation of multivariate distributions can be obtained by two steps: marginal distributions construction and copula estimation. This paper puts forward a smooth nonparametric copula estimation based on least squares support vector regression. By supplementing the classical least squares support vector regression with some additional shape-related constraints, this method tries to make the estimator satisfy three shape restrictions of copula: grounded, marginal and 2-increasing. Its training involves a simple convex quadratic programming problem, which can be solved in polynomial time. Experimental results clearly showed that this method could achieve significantly better performance than the classical least squares support vector regression and kernel smoother for copula estimation.