It is common practice for auditors to verify only a sample of recorded values to estimate the total error amount. Monetary-unit sampling is often used to over-sample large valued items which may be overstated. The aim is to compute an upper confidence bound for the total errors amount. Naïve bounds based on the central limit theorem are not suitable, because the distribution of errors are often very skewed. Auditors frequently use the Stringer bound which known to be too conservative. We propose to use weighted empirical likelihood bounds for Monetary-unit sampling. The approach proposed is different from mainstream empirical likelihood. A Monte–Carlo simulation study highlights the advantage of the proposed approach over the Stringer bound.

审计师通常只验证记录值的样本以估计总错误量。货币单位抽样通常用于对可能被夸大的大值项目进行过度抽样。目的是计算总误差量的置信上限。基于中心极限定理的朴素边界不适合，因为误差的分布通常非常倾斜。审计师经常使用众所周知过于保守的斯金格界限。我们建议对货币单位抽样使用加权经验似然界限。所提出的方法不同于主流的经验可能性。Monte-Carlo 模拟研究突出了所提出的方法相对于 Stringer 界限的优势。