Tightening CAV (DUS) Bounds by Using a Parametric Model
提出一种参数方法，通过识别会计总体中错误分布（如幂函数密度）来设定DUS抽样的总误差上界，相比非参数的Stringer界更高效，可减少审计过度。
This paper presents a new method of setting an upper bound for the aggregate error of an accounting population using Dollar Unit Sampling (DUS; see Anderson and Teitlebaum [1973]). The most widely used DUS bound setting procedure is the Stringer bound (Felix, Leslie, and Neter [1982]). This nonparametric procedure is popular because it produces a reliable bound for a wide variety of accounting populations. A disadvantage of the Stringer bound is that it is not statistically efficient, particularly when the population has low taintings. Other CA V (Combined Attributes and Variables) bounds have demonstrated the same problem (Reneau [1978]). A statistically inefficient bound may cause the auditor to conclude erroneously that the aggregate error may exceed a given material size, thereby promoting costly overauditing. The classical bound, an alternative distribution-free procedure that depends on the normality of the sampling distribution of the point estimator, is asymptotically efficient but unreliable in sample sizes used by auditors (Frost and Tamura [1986]). A simple parametric procedure is feasible if the underlying distribution of taintings is identified. We present an example of a parametric procedure using the power function density as a model and demonstrate the