The Open UniversitySkip to content
 

Multimodality on the geometric combination of Bayesian forecasting models

Faria, Alvaro and Mubwandarikwa, Emmanuel (2008). Multimodality on the geometric combination of Bayesian forecasting models. International Journal of Statistics and Management Systems, 3(1-2) pp. 1–25.

URL: http://www.math.binghamton.edu/arcones/ijsms/volum...
Google Scholar: Look up in Google Scholar

Abstract

We propose the geometric combination of predictive probability density functions as an alternative approach to the usually adopted linear combination. We show the conditions under which the geometric combination of Student t-densities is unimodal. Also, unlike the linear methods, geometric combinations are of closed form for densities from the regular exponential family and thus unimodal. A comparative analysis of linear and geometric combinations of predictive Student t{densities from regression dynamic linear models in a case of beverage sales forecasting in Zimbabwe shows the geometric method consistently producing skewed but unimodal densities. Consequences of decisions associated with both symmetric (quadratic and exponential) and non{symmetric (logarithmic) loss functions are investigated for multimodal linear combination densities when di®erent location parameters (means and largest modes) are chosen as point estimates. [brace not closed]

Item Type: Journal Article
ISSN: 0973-7395
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 12328
Depositing User: Àlvaro Faria
Date Deposited: 17 Nov 2008 13:29
Last Modified: 02 Dec 2010 20:15
URI: http://oro.open.ac.uk/id/eprint/12328
Share this page:

Actions (login may be required)

View Item
Report issue / request change

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk