Integrating statistical and nonstatistical audit evidence using belief functions: A case of variable sampling

Audit Decisions Using Belief Functions: A Review

This article provides an overview of the audit process along with the belief-function approach to audit decisions. In particular, the article highlights the advantages of using belief functions for representing uncertainties in the audit evidence and discusses the audit risk model of the American Institute of Certified Public Accountants as a plausibility model. Also, the article discusses the ...

Integrating Statistical and Non-Statistical Audit Evidence in Attribute Sampling Using Belief Functions

The main purpose of this article is to show how one can integrate statistical evidence from attribute sampling with non-statistical evidence within the Dempster-Shafer belief function framework. In particular, the article shows: (1) how to determine the sample size in attribute sampling to obtain a desired level of belief that the true attribute occurrence rate of the population lies in a given...

Structural Analysis of Audit Evidence using Belief Functions

This article performs two types of analysis using Dempster-Shafer theory of belief functions for evidential reasoning. The first analysis deals with the impact of the structure of audit evidence on the overall belief at each variable in the network, variables being the account balance to be audited, the related transaction streams, and the associated audit objectives. The second analysis deals ...

Why We Should Consider Belief Functions in Auditing Research and Practice

The Auditor's Report, Vol. 28, No. 2, March 2005 Why We Should Consider Belief Functions in Auditing Research and Practice Rajendra P. Srivastava, University of Kansas, and Theodore J. Mock, University of Southern California and Maastricht University Recent events in the auditing profession [sometimes called the ‘accounting’ profession] have clearly called for a reconsideration of the paradigms...

Integrating Statistical Audit Evidence with Belief Function Theory