Copula, marginal distributions and model selection: A Bayesian note

Ralph Dos Santos Silva, Hedibert Freitas Lopes

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


Copula functions and marginal distributions are combined to produce multivariate distributions. We show advantages of estimating all parameters of these models using the Bayesian approach, which can be done with standard Markov chain Monte Carlo algorithms. Deviance-based model selection criteria are also discussed when applied to copula models since they are invariant under monotone increasing transformations of the marginals. We focus on the deviance information criterion. The joint estimation takes into account all dependence structure of the parameters' posterior distributions in our chosen model selection criteria. Two Monte Carlo studies are conducted to show that model identification improves when the model parameters are jointly estimated. We study the Bayesian estimation of all unknown quantities at once considering bivariate copula functions and three known marginal distributions.

Original languageEnglish (US)
Pages (from-to)313-320
Number of pages8
JournalStatistics and Computing
Issue number3
StatePublished - Sep 2008
Externally publishedYes


  • Copula
  • Deviance information criterion
  • Marginal distribution
  • Measure of dependence
  • Monte Carlo study
  • Skewness

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics


Dive into the research topics of 'Copula, marginal distributions and model selection: A Bayesian note'. Together they form a unique fingerprint.

Cite this