Scale and shape mixtures of multivariate skew-normal distributions

Reinaldo B. Arellano-Valle, Clécio S. Ferreira, Marc G. Genton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.

Original languageEnglish (US)
Pages (from-to)98-110
Number of pages13
JournalJournal of Multivariate Analysis
Volume166
DOIs
StatePublished - Jul 2018

Keywords

  • EM-algorithm
  • Scale mixtures of normal distributions
  • Scale mixtures of skew-normal distributions
  • Shape mixtures of skew-normal distributions
  • Skew scale mixtures of normal distributions
  • Skew-normal distribution

ASJC Scopus subject areas

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

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