Confidence-credible intervals

Ivair R. Silva and Dionatan W. R. Oliveira

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 9, 2783-2802

Abstract: Frequentist and Bayesian approaches for interval estimation usually produce conflicting results if applied to analyze the same data set. Paradoxically, there is no unanimity in the literature on whether frequentist and Bayesian approaches are indeed concurrent theories. Thus, a fundamental question arises: frequentist and Bayesian approaches for interval estimation could be somehow reconciled? This paper offers an affirmative response for this question. Furthermore, we introduce a reconciling solution based on a hybrid frequentist-Bayesian interval estimator, the ‘confidence-credible interval’. The hybrid approach is simple and intuitive. It is also comprehensive in the sense of being applicable for any data probability distribution/likelihood shape, and for arbitrary prior distributions. An intensive simulation study shows the performance of the new methodology for the Gaussian and the Gamma distributions. The proposed method is illustrated through an application using real data in the light of state space models.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1780447 (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:9:p:2783-2802

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

DOI: 10.1080/03610926.2020.1780447

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 ().