Bivariate distributions with given extreme value attractor
Journal of Multivariate Analysis
Goodness-of-fit tests for copulas
Journal of Multivariate Analysis
Comparison of semiparametric and parametric methods for estimating copulas
Computational Statistics & Data Analysis
A goodness of fit test for copulas based on Rosenblatt's transformation
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
An Introduction to Copulas
On the simplified pair-copula construction - Simply useful or too simplistic?
Journal of Multivariate Analysis
Computational Statistics & Data Analysis
A goodness-of-fit test for Archimedean copula models in the presence of right censoring
Computational Statistics & Data Analysis
Estimating discrete Markov models from various incomplete data schemes
Computational Statistics & Data Analysis
Vine copulas with asymmetric tail dependence and applications to financial return data
Computational Statistics & Data Analysis
Unsupervised data classification using pairwise Markov chains with automatic copulas selection
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Copulas are used to model multivariate data as they account for the dependence structure and provide a flexible representation of the multivariate distribution. A great number of copulas has been proposed with various dependence aspects. One important issue is the choice of an appropriate copula from a large set of candidate families to model the data at hand. A large number of copulas are compared via likelihood principle, showing that it is hard to recognize the true underlying copula from real data since copulas with similar dependence properties are very close together. A goodness of fit test based on Mahalanobis squared distance between original and simulated log-likelihoods through parametric bootstrap techniques is also proposed. The advantage of this approach is that it is applicable to all families of copulas.