What is it about?

This paper proposes using methods of imprecise probability for statistical inference on the basis of confidence distributions. A confidence distribution is a probability distribution on parameter space that, unlike a Bayesian posterior distribution, does not require a prior distribution.

Featured Image

Why is it important?

Bayesian statistics has the advantage of flexibly determining estimates and other decisions using the principle of maximum expected utility or, equivalently, minimum posterior expected loss. The main drawback is its need for a prior distribution. This paper proposes a simple method of overcoming that disadvantage using confidence intervals.


The framework of this paper was eventually generalized into the theory of coherent fiducial distributions.

David R. Bickel
University of North Carolina at Greensboro

Read the Original

This page is a summary of: Coherent Frequentism: A Decision Theory Based on Confidence Sets, Communication in Statistics- Theory and Methods, April 2012, Taylor & Francis, DOI: 10.1080/03610926.2010.543302.
You can read the full text:




The following have contributed to this page