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Multivariate Student -t Regression Models: Pitfalls and Inference

Fernández,C.
; 
Steel,M.F.J.
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.
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Pagination: 28
Date
1997
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Publisher
Econometrics
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Tilburg University Research Output Collection
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Keywords
Bayesian inference, Coarse data, Continuous distribution, Maximum likelihood, Missing data, Scale mixture of Normals
Citation
Fernández, C & Steel, M F J 1997 'Multivariate Student -t Regression Models : Pitfalls and Inference' CentER Discussion Paper, vol. 1997-08, Econometrics, Tilburg.
URI
https://hdl.handle.net/20.500.14602/26394
License
info:eu-repo/semantics/restrictedAccess
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