LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wu, Juan; Wang, Xue; Walker, Stephen G. (2013)
Languages: English
Types: Article
Subjects: QA276

Classified by OpenAIRE into

arxiv: Statistics::Computation, Statistics::Other Statistics, Statistics::Methodology, Statistics::Theory, Computer Science::Databases
A copula can fully characterize the dependence of multiple variables. The purpose of this paper is to provide\ud a Bayesian nonparametric approach to the estimation of a copula, and we do this by mixing over a class\ud of parametric copulas. In particular, we show that any bivariate copula density can be arbitrarily accurately\ud approximated by an infinite mixture of Gaussian copula density functions. The model can be estimated by\ud Markov chain Monte Carlo methods and the model is demonstrated on both simulated and real data sets.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article