Credible Distributions of Overall Ranking of Entities

Oregon Convention Center 

Inference on overall ranking of a set of entities, such as chess players, subpopulations or hospitals, is an important problem. Estimation of ranks based on point estimates of means does not account for the uncertainty in those estimates. Treating estimated ranks without regard for uncertainty is problematic. We propose a Bayesian solution. It is competitive with recent frequentist methods, and more effective and informative, and is as easy to implement as it is to compute the posterior means and variances of the entity means. Using credible sets, we created novel credible distributions for the rank vector of the entities. We evaluate the Bayesian procedure in terms of accuracy and stability in two applications and a simulation study. Frequentist approaches cannot take account of covariates, but the Bayesian method handles them easily.