Assessment of Simple and Alternative Bayesian Ranking Methods Utilizing Parallel Computing

Abstract

The U.S. Census Bureau (USCB) assists the federal government in distributing approximately $400 billion of aid by providing a complete ranking of the states according to certain criteria, such as average poverty level. It is imperative that this ranking be as accurate as possible in order to ensure the fairness of the allocation of funds. Currently, the USCB ranks states based on point estimates of their true poverty level. Dr. Klein and Dr. Wright of the USCB have compared the performance of this method against more sophisticated procedures in simulation trials, but have found that they do not consistently outperform the existing method. We investigate this phenomenon by revisiting some of these procedures, and we expand on this work to produce new ranking algorithms. We utilize parallel programming to expedite Dr. Klein’s procedures. In addition, we specify two new prior distributions on the population means — using previous years’ census data as well as regression. We discuss the results of our methods in conjunction with Klein and Wright’s corresponding simulation results. In our final report, we compare the performance of our techniques to that of the USCB’s current method and show the resulting state ranks for each procedure.