Bayesian and robust Bayesian analysis in a general setting
Ali Karimnezhad and
Ahmad Parsian
Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 15, 3899-3920
Abstract:
In this paper we introduce a broad family of loss functions based on the concept of Bregman divergence. We deal with both Bayesian estimation and prediction problems and show that all Bayes solutions associated with loss functions belonging to the introduced family of losses satisfy the same equation. We further concentrate on the concept of robust Bayesian analysis and provide one equation that explicitly leads to robust Bayes solutions. The results are model-free and include many existing results in Bayesian and robust Bayesian contexts in the literature.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2018.1482344 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:48:y:2019:i:15:p:3899-3920
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2018.1482344
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().