Improved Asymptotic Formulae for Statistical Interpretation Based on Likelihood Ratio Tests
Abstract
In this work, we attempt to refine the classic asymptotic formulae to describe the probability distribution of likelihood-ratio statistical tests. The idea is to split the probability distribution function into two parts. One part is universal and described by the asymptotic formulae. The other part is case-dependent and is estimated explicitly using a 6-bin model proposed in this work. The latter is similar to performing toy simulations and can therefore predict the discrete structures in the probability distributions. The new asymptotic formulae provide a much better differential description of the test statistics. This improved performance is demonstrated in two toy examples for common likelihood ratio statistics.
Cite
@article{arxiv.2101.06944,
title = {Improved Asymptotic Formulae for Statistical Interpretation Based on Likelihood Ratio Tests},
author = {Li-Gang Xia and Yan Zhang},
journal= {arXiv preprint arXiv:2101.06944},
year = {2025}
}
Comments
version accepted for publication in Physica Scripta. Welcome to try it in your analysis and feedback. Thank you!