From Likelihood to Plausibility
This work addresses a statistical inference problem for researchers needing to evaluate evidence in complex hypothesis scenarios, representing an incremental extension of existing likelihood-based methods.
The paper tackles the limitation of likelihood ratios to simple hypotheses by proposing a general weight of evidence based on Dempster-Shafer plausibility, applicable to both simple and composite hypotheses, and demonstrates it through an urn problem and a real-world medical analysis.
Several authors have explained that the likelihood ratio measures the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for a simple hypothesis versus another simple hypothesis. However, the applicability of this idea is limited to simple hypotheses because the likelihood function is primarily defined on points (simple hypotheses) of the parameter space. In this paper we define a general weight of evidence that is applicable to both simple and composite hypotheses. It is based on the Dempster-Shafer concept of plausibility and is shown to be a generalization of the likelihood ratio. Functional models are of a fundamental importance for the general weight of evidence proposed in this paper. The relevant concepts and ideas are explained by means of a familiar urn problem and the general analysis of a real-world medical problem is presented.