@techreport{3fff240da5874537ba5f22dcadd3f3b1,

title = "Multivariate Student -t Regression Models: Pitfalls and Inference",

abstract = "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.",

keywords = "Bayesian inference, Coarse data, Continuous distribution, Maximum likelihood, Missing data, Scale mixture of Normals",

author = "C. Fern{\'a}ndez and M.F.J. Steel",

note = "Pagination: 28",

year = "1997",

language = "Dutch",

volume = "1997-08",

series = "CentER Discussion Paper",

publisher = "Econometrics",

type = "WorkingPaper",

institution = "Econometrics",

}