Multinomial-Dirichlet Bounds for Dollar-Unit Sampling in Auditing.
用贝叶斯方法结合审计师先验信息，构建会计总体中美元金额高估比例的误差界限，模拟表明贝叶斯界限在重复抽样中显著性水平接近名义水平，且计算效率不受样本量和错误数影响。
Abstract ABSTRACT: This paper takes a Bayesian approach to incorporate auditors' prior information in constructing error bounds for the proportion of dollar amount overstated (equivalently, the total amount overstated) in an accounting population. The multinomial distribution model within the dollar-unit sampling framework suggested by Fienberg, Neter, and Leitch [1977] is used, and the prior distribution belongs to the class of Dirichlet distributions. The properties of the resulting Bayesian bounds are discussed. Some comparisons of tightness of bounds are made with the multinomial and the modified multinomial bounds [Leitch, et al., 1982]. Simulation results suggest that some of the Bayesian bounds have good repeated sampling properties, in particular, in repeated sampling, a Bayesian bound gives a significance level close to the nominal level for many typical accounting populations. Another advantage of the suggested approach is computational efficiency which is independent of the sample size and the number of errors found in the sample.