A Quasi-Bayesian Audit Risk Model for Dollar Unit Sampling.

Abstract ABSTRACT: This paper presents a quasi-Bayesian model that generates a discrete posterior probability distribution on the expected total error in a population for any dollar unit sample and any given discrete or continuous prior probability distribution on the expected total error in a population. The model can be used with any sample size and any number of overstatements and understatements. In addition, it is the only dollar unit sampling evaluation procedure that can use data on the proportion of total dollars of each tainting found in the sample or known or assumed to exist in the population. Comparisons of the proposed and multinomial upper bounds are presented. These comparisons strongly suggest that the proposed model is a reasonable approach for evaluating dollar unit samples even if an informative prior probability distribution on the expected total error is not available.