Shape mixtures of multivariate skew-normal distributions

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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
Volume100
Issue number1
DOIs
StatePublished - Jan 1 2009

Keywords

  • 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

Fingerprint

Dive into the research topics of 'Shape mixtures of multivariate skew-normal distributions'. Together they form a unique fingerprint.

Cite this