A Note on a Comparison of Bayesian with Non-Bayesian Dollar-Unit Sampling Bounds for Overstatement Errors of Accounting Populations.
比较了贝叶斯与非贝叶斯货币单位抽样方法在审计中评估会计总体高估错误的表现，发现即使使用无信息先验，某些贝叶斯模型仍可能优于非贝叶斯方法。
Abstract ABSTRACT: There has been a proliferation of dollar-unit sampling bounds over the past few years; however, in many cases there has been little guidance provided on the relative performances of these bounds under representative audit conditions. This is particularly true of the Bayesian bounds which are analyzed here. One purpose of this study, therefore, is to provide further validation of Bayesian models by assessing their robustness when using diffuse priors on the "typical tainting patterns" of Leitch, et al. [1982]. A second purpose of the study is to compare Bayesian bounds with non-Bayesian bounds. The study found that even with diffuse priors, some Bayesian models show potential for improving on the performance of non-Bayesian dollar-unit sampling techniques.