Sampling and Estimation Methods with Regulatory Considerations in Tax and Healthcare Audits

Combining perspectives from statistical experts in taxation and healthcare audits, this session discusses recent statistical developments and regulatory considerations in sampling and estimation in applications in these fields. The papers will address a variety of important topics such as survey sampling and estimation methods including the efficacy of using Bayesian estimation, which is yet to be adopted for estimations in tax applications, and recent methodological developments and their implementations in R for conducting healthcare audits, which includes the minimum-sum method, conservative penny sampling, and an empirical likelihood algorithm. The session will also discuss current federal Tax Court cases involving statistical sampling and estimation. The papers focus on comparison of the efficacy of estimation methods across typical tax and healthcare audit populations found in practice, regulator considerations, and the potential repercussions of court cases. The court cases involve recent rulings and are ongoing, the Bayesian paper is an application of an old method to a new subject area. The regulator considerations, and repercussions include current and possible future directions. The papers will have a broad appeal to those practicing survey sampling and estimation methods (not just those in tax or healthcare audits). The suite of functions in samptest are applicable beyond healthcare audits. The regulatory considerations would be of interest to statisticians who are performing estimation that is subject to regulation or are themselves the regulators/writers of statistical standards and acceptable practices.