Copula density estimation by total variation penalized likelihood with linear equality constraints
Computational Statistics & Data Analysis
Nonparametric bivariate copula estimation based on shape-restricted support vector regression
Knowledge-Based Systems
Smooth Nonparametric Copula Estimation with Least Squares Support Vector Regression
Neural Processing Letters
Modeling financial dependence with support vector regression
Intelligent Data Analysis
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This paper deals with the problem of multivariate copula density estimation. Using wavelet methods we provide two shrinkage procedures based on thresholding rules for which knowledge of the regularity of the copula density to be estimated is not necessary. These methods, said to be adaptive, have proved to be very effective when adopting the minimax and the maxiset approaches. Moreover we show that these procedures can be discriminated in the maxiset sense. We provide an estimation algorithm and evaluate its properties using simulation. Finally, we propose a real life application for financial data.