期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2014
卷号：28
期号：2
页码：288-299
DOI：10.1214/12-BJPS207
语种：English
出版社：Brazilian Statistical Association
摘要：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.
