A skew Gaussian decomposable graphical model

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

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)


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
Pages (from-to)58-72
Number of pages15
JournalJournal of Multivariate Analysis
Early online date1 Mar 2016
Publication statusPublished - Mar 2016


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


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