Asymptotic comparison of some Bayesian information bounds

Ken-ichi Koike

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 3, 599-609

Abstract: There are many versions of Bayesian Cramér-Rao type lower bounds of the Bayes risk. We compare them from the point of view of asymptotic optimality. We show that the asymptotic optimality result still holds true in the sense of Bhattacharyya type lower bound in univariate case. And we show the asymptotic optimal choice in the Gill-Levit bound in multivariate case. As an application of it, we also show a lower bound for the Bayesian risk with a nuisance parameter.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1752722 (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:51:y:2022:i:3:p:599-609

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1752722

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 ().