A Lower Multinomial Bound for the Total Overstatement Error in Accounting Populations
Article Abstract:
Often account balances need to be adjusted to accurately reflect the true state of the account. Sampling procedures have been proposed for sampling line items in an account to determine the total error amount in the account. If the total error amount exceeds a predetermined amount the account may contain material errors. The question is what should the error amount be. A methodology has been developed that allows the determination of lower multinomial bounds for the total error amount. Tables showing various distributions and related computational results are presented.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1984
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Effect of Risk Aversion on Single Sample Attribute Inspection Plans
Article Abstract:
Bayesian single sampling inspection plans involve minimizing expected total losses when the prior distribution of batch quality and the sampling distribution are specified. The choice of an optimal sampling plan is impacted by a decision maker's aversion to risk. Comparisons are then made with classical linear cost models. Differences are identified. An optimization algorithm is discussed. It develops an optimal sampling plan based on maximizing the expected utility. Tables of numerical results are available.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1984
User Contributions:
Comment about this article or add new information about this topic:
A Lower Multinomial Bound for the Total Overstatement Error in Accounting Populations
Article Abstract:
In accounting, there is a need for a more accurate adjustment of account balances to reflect the true state of the account. A procedure for obtaining a lower multinomial bound on the total overstatement or understatement error in a population is developed. The effectiveness of the lower multinomial bound is compared with the Stringer bound. Areas covered include the nesting of probability terms, requirements of the optimization algorithm, and the clustering of sample errors. Tables are given.
Publication Name: Management Science
Subject: Business, general
ISSN: 0025-1909
Year: 1984
User Contributions:
Comment about this article or add new information about this topic:
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