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dc.contributor.authorFúquene, Jairo
dc.contributor.authorPérez, María Eglée
dc.contributor.authorPericchi Guerra, Luis Raúl
dc.date.accessioned2015-11-21T21:27:55Z
dc.date.available2015-11-21T21:27:55Z
dc.date.issued2014
dc.identifier.issnVol.28, Num.2
dc.identifier.urihttp://hdl.handle.net/123456789/2406
dc.descriptionPost-print. Not publisher's version PDF.
dc.description.abstractIn this paper, we propose a new wide class of hypergeometric heavy tailed priors that is given as the convolution of a Student-t density for the location parameter and a Scaled Beta 2 prior for the squared scale parameter. These priors may have heavier tails than Student-t priors, and the variances have a sensible behaviour both at the origin and at the tail, making it suitable for objective analysis. Since the representation of our proposal is a scale mixture, it is suitable to detect sudden changes in the model. Finally, we propose a Gibbs sampler using this new family of priors for modelling outliers and structural breaks in Bayesian dynamic linear models. We demonstrate in a published example, that our proposal is more suitable than the Inverted Gamma’s assumption for the variances, which makes very hard to detect structural changes.
dc.language.isoen_US
dc.publisherBrazilian Journal of Probability and Statistics
dc.subjectBayesian inference
dc.subjectrobust priors
dc.subjectScaled Beta 2 distribution
dc.subjectStudent t distribution
dc.subjectdynamic linear models
dc.subjectchange point detection
dc.subjectInverted-Gamma distribution
dc.titleAn alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models
dc.typeArticle


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