Bayesian Rank-Clustering.

ABSTRACT: This article proposes a new statistical model to infer interpretable population-level preferences from ordinal comparison data. Such data is ubiquitous, e.g., ranked choice votes, top-10 movie lists, and pairwise sports outcomes. Traditional statistical inference on ordinal comparison data results in an overall ranking of objects, e.g., from best to worst, with each object having a unique rank. However, the ranks of some objects may not be statistically distinguishable. This could happen due to insufficient data or to the true underlying object qualities being equal. Because uncertainty communication in estimates of overall rankings is notoriously difficult, we take a different approach and allow groups of objects to have equal ranks or be rank-clustered in our model. Existing model

SUBMITTER: Pearce M 

PROVIDER: S-EPMC12483714 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature