Shape mixtures of multivariate skew-normal distributions

Reinaldo B. Arellano-Valle, Marc G. Genton*, Rosangela H. Loschi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point of view, the results obtained in this paper bring tractability to the problem of inference for the shape parameter, that is, the posterior distribution can be written in analytic form. Robust inference for location and scale parameters is also obtained under particular conditions.

Original languageEnglish (US)
Pages (from-to)91-101
Number of pages11
JournalJournal of Multivariate Analysis
Issue number1
StatePublished - Jan 2009
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank two referees whose comments and suggestions have contributed to the improvement of the paper. The research of R.B. Arellano-Valle was supported in part by FONDECYT (Chile), grants 1040865, 7060133 and 7070137. The work of M.G. Genton was partially supported by NSF grant DMS-0504896 and by a grant from the Swiss National Science Foundation. R.H. Loschi acknowledges CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) of the Ministry for Science and Technology of Brazil, grants 3004505/2006-4, 472066/2004-8 and 472877/2006-2, for a partial allowance to her research.


  • 62E15
  • 62H05
  • Bayes
  • Conjugacy
  • Regression model
  • Robustness
  • Shape parameter
  • Skew-normal distribution
  • Skewness

ASJC Scopus subject areas

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


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