Robust likelihood methods based on the skew-t and related distributions

Adelchi Azzalini*, Marc Genton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

175 Scopus citations


The robustness problem is tackled by adopting a parametric class of distributions flexible enough to match the behaviour of the observed data. In a variety of practical cases, one reasonable option is to consider distributions which include parameters to regulate their skewness and kurtosis. As a specific representative of this approach, the skew-t distribution is explored in more detail and reasons are given to adopt this option as a sensible general-purpose compromise between robustness and simplicity, both of treatment and of interpretation of the outcome. Some theoretical arguments, outcomes of a few simulation experiments and various wide-ranging examples with real data are provided in support of the claim.

Original languageEnglish (US)
Pages (from-to)106-129
Number of pages24
JournalInternational Statistical Review
Issue number1
StatePublished - Apr 1 2008


  • Kurtosis
  • Maximum likelihood
  • Multivariate distributions
  • Profile likelihood
  • Robustness
  • Singular information matrix
  • Skewness

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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