Osiewalski, Jacek and Steel, Mark F. J. (1993) Robust bayesian inference in elliptical regression models. Journal of Econometrics, Volume 57 (Number 1-3). pp. 345-363. doi:10.1016/0304-4076(93)90070-L

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Abstract

Broadening the stochastic assumptions on the error terms of regression models was prompted by the analysis of linear multivariate t models in Zellner (1976). We consider a possibly nonlinear regression model under any multivariate elliptical data density, and examine Bayesian posterior and predictive results. The latter are shown to be robust with respect to the specific choice of a sampling density within this elliptical class. In particular, sufficient conditions for such model robustness are that we single out a precision factor τ2 on which we can specify an improper prior density. Apart from theB posterior distribution of this nuisance parameter τ2, the entire analysis will then be completely unaffected by departures from Normality. Similar results hold in finite mixtures of such elliptical densities, which can be used to average out specification uncertainty.

Item Type:	Journal Article
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Journal of Econometrics
Publisher:	Elsevier BV * North-Holland
ISSN:	0304-4076
Official Date:	May 1993
Dates:	Date Event May 1993 Published
Volume:	Volume 57
Number:	Number 1-3
Page Range:	pp. 345-363
DOI:	10.1016/0304-4076(93)90070-L
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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