Multivariate Student -t Regression Models: Pitfalls and Inference

C. Fernández, M.F.J. Steel

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We consider likelihood-based inference from multivariate regression models with independent Student-t errors. Some very intruiging pitfalls of both Bayesian and classical methods on the basis of point observations are uncovered. Bayesian inference may be precluded as a consequence of the coarse nature of the data. Global maximization of the likelihood function is a vacuous exercise since the likelihood function is unbounded as we tend to the boundary of the parameter space. A Bayesian analysis on the basis of set observations is proposed and illustrated by several examples.
Original languageDutch
Place of PublicationTilburg
Number of pages28
Publication statusPublished - 1997

Publication series

NameCentER Discussion Paper


  • Bayesian inference
  • Coarse data
  • Continuous distribution
  • Maximum likelihood
  • Missing data
  • Scale mixture of Normals

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