A skew Gaussian decomposable graphical model

Hamid Zareifard*, Håvard Rue, Majid Jafari Khaledi, Finn Lindgren

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper proposes a novel decomposable graphical model to accommodate skew Gaussian graphical models. We encode conditional independence structure among the components of the multivariate closed skew normal random vector by means of a decomposable graph so that the pattern of zero off-diagonal elements in the precision matrix corresponds to the missing edges of the given graph. We present conditions that guarantee the propriety of the posterior distributions under the standard noninformative priors for mean vector and precision matrix, and a proper prior for skewness parameter. The identifiability of the parameters is investigated by a simulation study. Finally, we apply our methodology to two data sets.

Original languageEnglish (US)
Pages (from-to)58-72
Number of pages15
StatePublished - Mar 1 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Inc.


  • Conditional independence
  • Decomposable graphical models
  • Multivariate closed skew normal distribution
  • Noninformative prior

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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