Bayesian confidence intervals with more power to estimate the minimum effect and determine the sign

Haibing Zhao

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 3, 636-648

Abstract: In this article, we propose two confidence intervals (CIs) for a single parameter, which can represent an effect in practice, through a Bayesian decision theoretic approach for different goals. One balances length and separating zero from a CI. The other one balances length and the ability of making direction decision of a CI. Two loss functions are constructed by adding suitable penalty terms into the traditional loss function. Then two CIs are obtained by minimizing the loss functions. When compared with the traditional Bayesian CI, the first CI has more chance to estimate the minimum effect; the second CI has more chance to make directional decision for the parameter. General steps for minimizing the loss functions to obtain the CIs are provided. A real data are analyzed through constructing the second CI.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1752725 (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:636-648

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

DOI: 10.1080/03610926.2020.1752725

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 ().