Name
Abstract | ||
|---|---|---|
While parametric copulas often lack expressive capacity to capture the complex dependencies that are usually found in empirical data, non-parametric copulas can have poor generalization performance because of overfitting. A semiparametric copula method based on the family of bivariate Archimedean copulas is introduced as an intermediate approach that aims to provide both accurate and robust fits. The Archimedean copula is expressed in terms of a latent function that can be readily represented using a basis of natural cubic splines. The model parameters are determined by maximizing the sum of the log-likelihood and a term that penalizes non-smooth solutions. The performance of the semiparametric estimator is analyzed in experiments with simulated and real-world data, and compared to other methods for copula estimation: three parametric copula models, two semiparametric estimators of Archimedean copulas previously introduced in the literature, two flexible copula methods based on Gaussian kernels and mixtures of Gaussians and finally, standard parametric Archimedean copulas. The good overall performance of the proposed semiparametric Archimedean approach confirms the capacity of this method to capture complex dependencies in the data while avoiding overfitting. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.csda.2011.01.018 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
bivariate archimedean copula,archimedean copulas,parametric copula,complex dependency,semiparametric dependence model,flexible copula method,natural cubic splines,proposed semiparametric archimedean approach,copula estimation,parametric copula model,regular variation,tail dependence,semiparametric estimator,archimedean copula,non-parametric copula,cubic spline,mixture of gaussians,gaussian kernel | Econometrics,Tail dependence,Copula (linguistics),Parametric statistics,Semiparametric model,Overfitting,Estimation theory,Bivariate analysis,Statistics,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
55 | 6 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Miguel Hernández-Lobato | 1 | 613 | 49.06 |
| Alberto Suárez | 2 | 487 | 22.33 |