Translator Disclaimer
May 2014 An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models
Jairo Fúquene, María-Eglée Pérez, Luis R. Pericchi
Braz. J. Probab. Stat. 28(2): 288-299 (May 2014). DOI: 10.1214/12-BJPS207

Abstract

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.

Citation

Download Citation

Jairo Fúquene. María-Eglée Pérez. Luis R. Pericchi. "An alternative to the Inverted Gamma for the variances to modelling outliers and structural breaks in dynamic models." Braz. J. Probab. Stat. 28 (2) 288 - 299, May 2014. https://doi.org/10.1214/12-BJPS207

Information

Published: May 2014
First available in Project Euclid: 4 April 2014

zbMATH: 1319.62031
MathSciNet: MR3189499
Digital Object Identifier: 10.1214/12-BJPS207

Rights: Copyright © 2014 Brazilian Statistical Association

JOURNAL ARTICLE
12 PAGES


SHARE
Vol.28 • No. 2 • May 2014
Back to Top