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A Bayesian Solution for a Statistical Auditing Problem
Journal of the American Statistical Association
Vol. 98, No. 463 (Sep., 2003), pp. 735-740
Stable URL: http://www.jstor.org/stable/30045301
Page Count: 6
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Auditors often consider a stratified finite population where each unit is classified as either acceptable or in error. Based on a random sample, the auditor may be required to give an upper confidence bound for the number of units in the population that are in error. In other cases the auditor may need to give ap value for the hypothesis that at least 5% of the units in the population are in error. Frequentist methods for these problems are not straightforward and can be difficult to compute. Here we give a noninformative Bayesian solution for these problems. This approach is easy to implement and is shown to have good frequentist properties.
Journal of the American Statistical Association © 2003 American Statistical Association