| dc.creator | Fúquene, Jairo | |
| dc.creator | Pérez, María Eglée | |
| dc.creator | Pericchi Guerra, Luis R. | |
| dc.date | 2014-04-11T18:57:00Z | |
| dc.date | 2014-04-11T18:57:00Z | |
| dc.date | 2014 | |
| dc.date.accessioned | 2017-03-17T16:54:05Z | |
| dc.date.available | 2017-03-17T16:54:05Z | |
| dc.identifier | Vol.28, Num.2 | |
| dc.identifier | http://hdl.handle.net/10586 /356 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/647492 | |
| dc.description | Post-print. Not publisher's version PDF. | |
| dc.description | In 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 | en_US | |
| dc.publisher | Brazilian Journal of Probability and Statistics | |
| dc.subject | Bayesian inference | |
| dc.subject | robust priors | |
| dc.subject | Scaled Beta 2 distribution | |
| dc.subject | Student t distribution | |
| dc.subject | dynamic linear models | |
| dc.subject | change point detection | |
| dc.subject | Inverted-Gamma distribution | |
| dc.title | An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models | |
| dc.type | Artículos de revistas | |
