Bounds for monetary-unit sampling in auditing: an adjusted empirical likelihood approach

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.