Optimal confidence intervals for bounded parameters (a correct alternative to the recipe of Feldman and Cousins)

TL;DR

This paper introduces a method for constructing confidence intervals that incorporate prior bounds on parameters, improving accuracy and robustness over traditional methods like Feldman and Cousins.

Contribution

It presents a straightforward, optimal approach to include a priori bounds in confidence intervals within the frequentist framework, enhancing resolution and robustness.

Findings

Provides a method with improved resolution for non-boundary values

Ensures robustness against non-physical estimator values

Offers a straightforward alternative to Feldman and Cousins' recipe

Abstract

A priori bound for the parameter to be estimated is incorporated into confidence intervals within frequentistic approach in a straightforward and optimal fashion, ensuring the best resolution of non-boundary values as well as robustness for non-physical values of the estimator.