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Robust bayesian inference in elliptical regression models
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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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Official URL: http://dx.doi.org/10.1016/0304-4076(93)90070-L
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 | ||||
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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: |
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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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