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IV. BIBLIOGRAPHY OF STATISTICAL PRACTICE IN AUDITING 1. Alphabetical Annotated Bibliography Aitchison, I. (1955~. On He Distribution of a Positive Random V an able Having a Discrete Probability Mass at He Origin. Journal of the American Statistical Association, 50: 901-908. This paper is the first to address He problem of the estimation of parameters from data containing many zeros. The best unbiased estimators of the population mean and the variance are derived. However, the paper does not consider me sampling distnbution of these estimators and Gus results are not of immediate use to auditors. American Institute of Certified Public Accountants (ATCPA). (1981~. Statement on Auditing Starboards (SAS No. 391. New York: AICPA. American Institute of Certified Public Accountants (ATCPA). (1983~. Audit Sampling. New York: AICPA. Auditing practices are regulated by a number of national organizations. Among them, the most influential organization is the American Institute of Certified Public Accountants, a national organization of practicing certified public accountants. The current auditing standards are stated in: Statement on Auditing Standards (SAS No. 391. Audit Sampling is a detailed interpretation of SAS No. 39 and descnbes procedures and practicing guides that auditors should adhere to when me auditing is performed based on examination of less than 100% of the items of an accounting balance. Anderson, R. J. and Teitlebaum, A. D. (1973~. DolUar-unit Sampling. Canadian Chartered Accountant (after 1973, this publication became CA Magazine ), April: 30-39. This expository article introduces dolHar unit sampling in a way understandable to a broader group of researchers and practitioners. Anderson, R. I. and Leslie, D. A. (1975~. Discussion of Consideration in Choosing Statistical Sampling Procedures in Auditing. Journal of Accounting Research, Supplement: 53-64. 60

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The discussion focuses partly on the paper by Loebbecke and Neter and partly on tile ~en-for~coming AT CPA Monograph No. 2, Behavior of Major Statistical Estimators in Sampling Accounting Populations. With respect to He former, Anderson and Leslie question He distinction between whether an audit should have "attributes" or"vanables" objectives, arguing that an audit objectives should be expressed in monetary teens. Wig respect to the latter study, Anderson and Leslie argue Hat He ATCPA study should have included doBar-un~t sampling with He Stnnger bound, rather than the combined attributes-vanables bound because He Stnnger bound is less conservative and more widely used. The discussants believe mat doBar-unit sampling wid1 He Stnnger boulKl is appropnae in almost all circumstances so that He auditor need not consider altemative sampling approaches and fall-back procedures if the anticipated environmental conditions are not met, as proposed by Loebbecke and Neter. Baker, R. L. and Copeland, R. M. (1979). Evaluation of the Stratified Regression Estimator for Auditing Accounting Populations Journal of Accounting Research, 17: 606- ~ 7. This study supplements the Neter and Loebbecke AICPA Monograph No. 2 by studying the behavior of the stratified regression estimation for the same four accounting populations used in the AICPA study. The precision of me regression estimator in general tends to be almost the same as the precision of the stratified difference, ratio, and regression estimators. As for these other estimators, the reliability of the nominal confidence coefficient for the stratified regression estimator is poor at low error rates, win the regression estimator performing even more poorly than the other estimators. Barkman, A. (19771. Within-Item Vanation: A Stochastic Approach to Audit Uncertainty. The Accounting Review, 52: 450464. The author proposes that the audit amount to be established by the auditor for a line item be treated as a random variable, such as when the line item is the amount of bad debt for an account receivable. The author assumes that the distribution reflecting the uncertainty of the line item audit amount is given by the beta distnbution and that the distnbution of the total amount is normal. A simulation study was camed out to study the behavior of sample estimates under different population conditions for 61

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mean-per-un~t and difference estimators. Beck, P. I. (1980~. A Cntical Analysis of the Regression Estimator in Audit Sampling. Journal of Accounting Research, IS: 16-37. This study supplements the Neter and T-oebbecke study In ATCPA Research Monograph No. 2 by examining We behavior of the stratified and unstratified regression estimators for the accounting populations considered in me ATCPA monograph. ~ addition, one of these populations was filcher manipulated in order to vary the extent of heteroscedasticity in the population. The results obtained were similar to those previously reported for me difference and ratio estimators in the Never and Loebbecke sly. The author concludes mat heteroscedasticity appear to be a significant factor in the behavior of He regression estimator in two of the four accounting populations and mat stratification cannot always be relied upon to provide a confidence level close lo the nominal one. The author also made a limited study of me power of statistical tests based on the regression estimator. Burdick, R. K. and Reneau, I. H. (1978~. The Impact of Different Error Distributions on the Performance of Selected Sampling Estimators in Accounting Populations. Proceedings of Business and Economic Statistics Section: 779-781, Washington, D.C.: Arnencan Statistical Association This paper reports on a simulation study based on one of me accounting populations employed In the Neter and Loebbecke (1975) study. Errors were injected into the population at different error rates, win equal probability for each line item, with probability proportional to book amount, and with probability inversely proportional to book amount. A number of estimators were studied as to their precision and me closeness of the actual confidence level to the nominal one based on large-sample theory. The authors conclude that an estimator developed by Hartley is to be preferred over me other estimators studied. Sampan, Lewis A. (1933~. The Efficacy of Tests. The American Accountant, December: 360-366. This paper proposes application of a simple probability model for computing He sampling risk in auditing and is the first publication of such an attempt in accounting. 62

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Cox, D. R. and SneD, E. I. (1979~. On Sampling and Me Estimation of Rare Errors. Biometrika, 66: 124-132. After describing a theoretical mode} of monetary unit sampling, me paper presents a Bayesian analysis of We problem. Spec~ficaDy, me authors consider me case where the number of errors has a Poisson distribution and the proportional error density is exponential. Using a simple conjugate pnor, they denve the posterior distribution and discuss some possibilities for the parameters of me prior distribution. Cox, D. R. and SneH, E. I. (19X2~. Correction Note on Sampling and the Estimation of the Rare Enors. Biometrika, 69: 491. Co~necdons to their 1979 paper are announced in this note. Cyert, R. M. and Davidson, H. Justin. (1962~. Statistical Samplingfor Accounting Information. Englewood Cliffs, New Jersey: Prentice- Hall, hnc. This basic text In statistical sampling for auditing introduces among other standard topics the application of sequential sampling for compliance test. Deakin, E. B. (1977~. Discussant's Response to Computing Upper Error Limits in Dollar Unit Sampling. Frontiers of Auditing Research, edited by B. E. Cushing and ]. L. Krogstad. Austin: Bureau of Business Research, The University of Texas at Austin: 19S-201. This critique of Garstka(1977b) mentions that the conservatism of upper error bounds should be reduced by research on the handling of unobserved errors, that me models proposed by Gars~ca lack empirical support, that the proposed use of altemative models provides no rationale for selecting an appropriate model in a given situation, and that He research does not provide sufficient evidence to support the assumption Cat generalized or compound Poisson models would be any more useful in auditing than the present dollar-unit sampling models. Duke, Gordon L., Neter, I. and Leitch, R.A. (19821. Power Characteristics of Test Statistics in He Auditing Environment: An Empincal Study. Journal of Accounting Research, 20 :42-67. The power characteristics of eight test statistics, used with both 63

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the positive and negative testing approaches, are studied for four different accounting populations. Two error characteristics linkage models were developed for systematically creating different total error amounts In an accounting population. It was found Mat no one test statistic and either of the two testing approaches is uniformly superior, and that audit decisions based on a sample of 100 observations tend to involve high sampling risks for me auditor. Dworin, L. and Gnmiund, R. A. (1984~. Dollar Unit Sampling for Accounts Receivable and Inventor. The Accounting Review, 59 :218-241. The development of the moment bound is discussed in detail in this article. The authors state Mat Weir methods are based on Me assumption that Me doBar unit tainting follows a mixture of two y2 distributions. A helpful chart is provided in Weir Table 1 for computing the bound. Its performance is compared with that of the mulunomial bound. Dwonn, L. and GnmIund, R. A. (1986~. Dollar Unit Sampling: A Comparison of me Quasi-Bayesian and Moments Bounds. The Accounting Review, 61 :36-57. This article reports the results of comparing the performance of the moments bound with that of McCray's quasi-Bayesian bound using the unifonn pnor. A slight modification is proposed in the original moment bound to make me bound more efficient without any noticeable Toss of the reliability of the bound. For me pop~anons considered, the true level of confidence tends to be higher than the nominal level of 95% used in the study for bow bounds. Silk, both bounds are considerably tighter than He Stanger bound. However, between He two, the perfonnance is relatively comparable. Felix, W. L., Jr., Leslie, D.A. and Neter, I. (19821. University of Georgia Center for Audit Research Monetary Unit Sampling Conference, March 24, 1981. Auditing: A Journal of Practice & Theory, I: 92-103. This paper reports He results of the conference on Dollar Unit Sampling held at the University of Georgia in March, 1981. A short summary of existing methods for computing bounds is given. Also, the advantages and disadvantages of DUS compared 64

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to line item sampling are discussed. Several DUS mess are presented and compared. Research issues are also su~nmanzed. Felix, W. Lo. Jr. and Gnmlund, R. A. (1977~. Sampling Model for Audit Tests of Composite Accounts. Journal of Accounnng Research, 15:2342. This article discusses an altemative statistical sampling model which avoids some of the assumptions of conventional mesons. It is a Bayesian approach where the book value and audit value are analy~caDy combined win the auditor's prior judgments. A single combined "bem-nomlal" probability distribution for Me total enor in an account balance is denved. Several properties of this distribution are presented, followed by a discussion of how the auditor may use it to make probabilistic statements about Me total enor amount in a population. The computational procedure for using Me beta-no~mal distribution is cumbersome, Bus several altemative computational procedures are suggested, followed by a brief discussion of how the analysis may be used to preselect a sample size. Festige, M. O. (1979~. Discussion of An Empirical Study of Error Charactenstics in Audit Populations. Journal of Accounting Research, 17 Supplement: 103-107. This is a discussion of the study by Ramage et al. (1979) by a practicing auditor. One of his comments is that the study may be biased because the audit data base used In the study is supplied by one of the major accounting firms and thus may reflect their audit objectives which may also vary from one case to another. Fienberg, S. E., Neter, I. and Leitch, R.A. (1977~. Estimating Be Tom Overstatement Error in Accounting Populations. Journal of the American Statistical Association, 72 :295-302. This paper presents a statistical sampling approach based on the multinomial distribution for obtaining a bound for either the total population overstatement or understatement, or both. The bound is denved by using an optimization routine that finds Be maximum monetary error subject to constraints representing the joint confidence region for the multinomial parameters. The key element is the definition of the S set which denotes me set of ad outcomes as extreme or less extreme Man the observed outcomes. The multinomial bound is numencaBy compared to Be Stringer 65

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bound and the results indicate that the multinomial bound is less conservative Man We Stringer bound. Financial Accounting Standards Board (FASB). (1980~. Statement of Financial Accounting Concepts No. 2 (SFAC2 - Qualitative Characteristics of Accounting Information). Stamford, Conn: FASB. The Securities Exchange Act of 1934 gave the SEC me authority to promulgate financial reporting standards (Generally Accepted Accounting Principles or GAAP) for Nose companies subject to the jurisdiction of the SEC. The SEC, in rum, has delegated this authority to the FASB. The definition of a material error is from paragraph 132 of SFAC2 issued in May, 1980. Frost, P. A. and Tamura, H. (1982~. Jackknifed Ratio Estimation in Statistical Auditing. Journal of Accounting Research, 20: 103-120. One recent development in statistics is the use of computer intensive methods for data analysis. In this article, Me performance of the ratio estimation is studied when the starboard enor is computed using the conventional method and using the jackknife. The accounting data used in the Neter and Loebbecke study are employed as the populations for simulation. Their conclusion is that when error rates are not too small, so that the problem of the mixture as discussed in the main text is not severe, the jackknife clearly gives a better performance and should be used. Frost, P. A. and Tamura, H. (1986~. Accuracy of Auxiliary InfoImation Interval Estimation in Statistical Auditing. Journal of Accounting Research, 24: 57-75. This work extends Kaplan's 1973 investigation of the performance of the auxiliary information interval estimators and traces the cause of the poor performance of these estimators to the skewness of the accounting population induced by the mass of probability at the ongin. Analysis is done based on the difference estimator but indicates that the result can be applicable to the ratio estimator. Frost' P. A. and Tamura, H. (1987~. Accuracy of Auxiliary Infonnation Interval Estimation In Statistical Auditing. Working Paper Series #2-87, Department of Management Science, School & 66

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Graduate Schools of Business A~nin~stration, University of Washington, Seance, WA 98195. This working paper contains additional results to those that are reported in Frost and Tamura (1986~. Garstka, S. I. (1977a). Models for Computing Upper Error Limits in DoDar-Urut Sampling. Journal of Accounang Research, 15 :179-92. This paper investigates alternative mesons of computing upper error limits for the monetary error in an accounting population. The compound Poisson process is used to model We error rate and We distnbunon of error sizes in the population. Simulations are used to demonstrate mat tighter upper error limits can be achieved using Bayesian procedures compared to the Stringer bound. Garstka, S. J. (1977b). Computing Upper Error Limits in DoBar-Un~t Sampling. Fronners of Auditing Research, edited by Cushing, B.E. and Krogstad, J.~. Austin: Bureau of Business Research, The University of Texas at Austin: 163-82. This paper poses a number of models to be used in conjunction win doBar-un~t sampling. In particular, six compound Poisson models and three generalized Poisson models are considered and upper error bounds developed for each. A simulation study is then used to examine the properties of the venous bounds. Use of prior information in selecting the appropriate Poisson model can lead to tighter upper error limits. Garstka, S. I. (19791. Discussion of An Empirical Study of Error Charactenstics in Audit Populations. Journal of Accounting Research,-17 Supplement: 103-113. Based on the argument that me characteristics of an accounting population should assist the auditor in selecting a proper estimator, the author comments that the measures reported in Me study by Ramage et al. (1979) may not be useful for the auditors. For example, he points out Cat the fractional errors are computed in teens of the audited amount as He base. However, the audited amount is not available for most of me items. Garstka, S. J. and Ohlson, P.A. (1979~. Ratio Estimation in Accounting Populations with Probabilities of Sample Selection 67

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Proportional to Size of Book Values, Journal of Accounting Research, 17: 23-59. The authors propose a modification of the standard PPS estimator of the population total monetary error. The modification involves denying a factor to use as a multiple of the standa~ ever in constructing an upper confidence limit for Me total monetary error. The basis for the factor is largely heuristic. Lunited simulation is used to test We performance of the procedure. Godfiey, I. T. and Neter, I. (1984~. Bayesian Bounds for Monetary Unit Sampling in Accounthng and Auditing. Journal of Accounting Research, 22: 497-525. In 1979 Cox and Sne]1 published a Bayesian mode} for analysis of Dollar Unit Sample data. This work investigates Me sensitivity of the Cox and Snell bound if me auditor's knowledge is incorporated by using different prior distnbutions, e.g., by using a Beta distribution instead of the Gamma distnbution as proposed by Cox arid SneD for the error rate. The authors observe that the effects are moderate. The authors, however, report that the Cox and SneB bound is sensitive to me choice of the prior parameter values. Using simulation, they conclude, it iS possible to find the prior parameter values for which the bound demonstrates a desirable relative frequency property. GooUfellow, I. L., Loebbecke, I. K. and Neter, I. (1974~. Some Perspectives on CAV Sampling Plans. Part I, CA Magazine, October: 93-30; Part II, CA Magazine, November: 46-53. Combined attr~buees-vanables (CAY) sampling plans were developed to overcome inadequacies In both attributes and variables sampling plans. CAV plans seek to combine the two approaches to obtain effective and efficient estimates of dollar errors in audit populations with low error rates. Part ~ of Me article discusses the basic concepts of CAV sampling. It explains how an attributes sampling plan of unstratified audit units can lead to upper dollar precision limits for Me population total overstatement, and how stratification of the mats improves the efficiency of the estimate. Finally, it considers units selected with probabilities proportional to the book amounts and the essentially equivalent procedures of unstratified random selection of dollar units. Pan I! explains how Me combined attributes 68

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vanables approach provides lighter precision limits. The strengths of CAV sampling plans are Hat Key provide dolBar precision estimates even when Me sample contains no error, incorporate the efficiency advantages of stratification without revering stratified selection, and rely on simple conceptual foundations. The weaknesses of the CAV approach include; Be unbalanced treatment of overstatement and understatement errors, inapplicability to sampling non-dollar audit units, ineffective design for disclosing errors, inadequacy of one-sided precision Innits for detenn~ng the amount of adjusunent Squired, assumption of a zero error rate for planning sample size, and emphasis on conservation of precision limits. - Gnmiund, R. A. and Felix. W. L. (1987~. Simulation Evidence and Analysis of Altemative Methods of Evaluating Dollar-Un~t Samples. The Accounting Review, 62 :455479. The long run performances of thme Bayesian bounds and the moment bolmd by Dwonn and Gnmiund are compared. Thme Bayesian models are: the Nonnal error model as developed by Gnmiund and Felix, the Cox and SnelB bound and Be multinomial bound with the Dinchlet nnor hv T~l.i P. "' The populations used for simulation utilize the mode] descnbed in Dwonn and Grimiund (1984~. The non-zero tastings are specified by a mixture of x2 distnbutions and include negative values. The performance of a Bayesian bound depends on the prior parameter values. However, for the prior settings used in this study the Bayesian nonnal error mode] and He multinomial bound indicate more consistent performance than the Cox and SneP bound. The multinomial bound with the Dinchiet prior is reported to be too conservative. rat ~ -- ~. ma. ^ ,. Ham, J., hostel, D. and Smieliauskas, W. (19851. An Empirical Study of Error CharactensUcs in Accounting Populations. The Acenun~inP Review, 60: 387406 . ,0 The empirical study of audit populations is scarce and this work is one of four such studies published. While the previous We studies used the data base supplied by one major accounting finn, this work is based on the data from another major accounting firm. Three factors are considered as possibly affecting me error distnbution: (1) account category, (2) company size and (3) industry . 69

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Hylas, R. E. arid Ashton, R. H. (1982). Audit Detection of Financial Statement Erwrs. The Accounting Review, 57: 751-765. Based on 152 audit cases of one of major public accounting finns the causes of errors are traced. In these 152 audits 281 errors requinng financial statement adjustments were fourth. The error causes are classified into seven categories and their frequencies are reported. Intemal Revenue Service. (1972~. Audit Assessments Based on Statistical Samples (Memorandum to Assistant Commissioner from Chief Counsel). Washington, D.C.: IRS Intema1 Revenue Service. (1975~. Audit Assessments Based on Statistical Samples - Supplemental Memorandum (March 6 Memorandum to Chief Counsel from Director, Refund Litigation Division & Acting Director, Tax Court Litigation Division). Washington, D. C.: IRS. The legal ramifications of statistical sampling for tax audit are studied in these documents and the opinion of the Chief Counsel is stated. It is concluded that "although the propriety of the use of such techniques is not free from doubt, there is sufficient merit in the proposal to warrant judicial testing". Johnson, J. R., Leitch, R. A and Neter' I. (1981~. Characteristics of Errors in Accounts Receivable and Inventory Audits. The Accounting Review, 56 :270-293. Auditors and accountants require empinca1 information about He characteristics of audit populations and error distnbutions to plan the audit strategy. There is a need for information about He relative frequency, magnitude, distribution, and possible causes of errors. In this article, the enor characteristics and the relationship between errors and book values in 55 accounts receivable and 26 inventory audits are examined. The distnbutions of the error amounts and error tintings were studied, as well as the relation between ever amounts and book amounts. A summary of the findings is; (i) Here is great vanability in error rates, wide those of inventory audits tending to be much higher, (ii) evidence suggests Blat the error rates may be higher for larger accounts and for accounts with larger line items; (iii) most errors in receivable audits are overstatements, while in inventory audits, overstatements and understatements are more balanced in number, (iv) the distribution of error amounts are far 70

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128. This work compams We long run performance of venous Bayesian and non-Bayesian bounds. Smith, T. M. F. (1976~. Statistical Sampling for Accountants London: Accountancy Age Books This text covers the major topics In statistical sampling for accountants and auditors. Additionally, Me last chapter in Me book (Chapter 14), describes the problem of sampling for rare events. Monetary units sampling is described as a technique for coping with the auditor's problem of determining me monetary error when those errors are rare. Criticism of monetary unit sampling is also presented, particularly as related to Me effect of the selected doBar being a part of an account balance or transaction that represents Me audit unit. Smith, T. M. F. (1979~. Statistical Sampling in Auditing: a Stai~stician's Viewpoint. The Statistician, 28 :267-280. The statistical problems mat arise in auditing are summanzed and the validity of Dollar Unit Sampling is discussed. The Cox and SneB infinite population mode] is presented as me only available theoretical justification for DUS. Stringer, K. W. (19631. Practical Aspects of Staiishcal Sampling in Auditing. Proceedings of the Business and Economic Statistics Section :405411, Washington, D. C.: American Statistical . . Assoclanon This paper describes some difficulties of using statistical procedures based on normal theory in many auditing situations where monetary errors are rare. While few details are given there is a brief description of me methodology now known as monetary unit sampling. Stnnger, K. W. (1979~. Statistical Sampling In Audinng-The State of He Art Annual Accounting Review, 1:~ ~ 3-127. It is fair to say that the author is most instnunental In introducing statistical sampling in auditing. This article reviews its histoncal development. He also predicts that use of statistical sampling, particularly of Dollar Unit Sampling, win expand in auditing 81

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practice and calls for msearch to develop more efficient bounds. Tamura, H. (1985). Analysis of He Garstka-Ohison Bounds. Auditing: A Journal of Practice & Theory, 4: 133-142. This article comments on a property of He Gars~a-Ohison bound. It is demonstrated Hat He bound may not work because it does not take into account the skewness of the sampling distribution of the estimator. Tamura, H. and Frost, P.A. (1986~. Tightening CAV (DUS) Bounds by Using a Pararnewc Model. Journal of Accounting Research, 24: 364-371. A potentially profitable application of computer intensive data analysis is in approximating He small sample sampling distnbution. In this article the authors apply the par~etnc bootstrap to determine the sampling distribution of He estimator of He mean tainting. A power function is proposed for modeling the faintings. Their model is described in Section II.6 above. Tarnura, H. (1988~. Estimation of Rare Errors Using Expert Judgement. Biometrika, 7S To appear. A nonparametac Bayesian model is proposed using Ferguson's DinchIet process to specify the prediction of the conditional distnbution of the error. The distnbudon of the conditional mean of the error is obtained by numerically inverting the characteristic function. The error rate is modeled by a beta distnbution. The distribution of the mean error is derived by taking the expectation of the mean of He conditional en or over the error rate. Numencal examples are given and comparisons with parametric models are discussed. The model is discussed in Section II.7 above. Teitlebaum, A. D., Leslie, D. A. and Anderson, R. I. (1975~. An Analysis of Recent Commentary on Doldar-Unit Sampling in Auditing, McGill University working paper, March. This paper is a response to tile two-part article in the October and November 1974 issues of CA Magazine by GooUfeBow, Loebbecke, and Neter. Issues under contention to which responses are made In this paper include He planning of sample size win dollar-unit sampling, the handling of over- and understatement errors, the method of sample selection, He 82

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evaluation of an upper bound, and a compan son of monetary t sampling with the Stringer bound and line-item sampling with variables estimation. Tittenngton, D. M., Smith, A. F. M. and Makov, U. E. (1985). Stadst~cal Analysis of Finite Mixture Distributions. New Yoric: John Wiley. An extensive list of references on mixture is provided In this wed organized exposition of Me subject Tsui, K. W., Matsumura, E. M and Tsui, K. L. (19X5~. Muldnomial- Dinchiet Bounds for Dollar Unit Sampling in Auditing. The Accounting Review, 60 :76-96. The multinomial bound developed by Fienberg et al. (1977) is difficult to compute. It is also subject to the definidon of We S set. ~ this article, the authors develop a Bayesian approach to ache problem. The model is described In Section Il.7 above. van Heerden, A. (1961~. Steekproeven als Midde] van Accountantscontrolex (Statistical Sampling as a Means of Auditing). Maar~blad voor Accountancy en Bedrijishuishou~kur~e, I} :453. This is He earliest known publication proposing the use of monetary unit sampling in auditing. The suggested evaluation technique involved regarding the monetary uruts within any audit as being either correct or in en on For example, if an audit unit win a recorded amount of 100 doBars had an audited amount of 80 doDars, the 80 doBars of the 100 doBars were regarded as correct and the last 20 dollars were regarded as incorrect. 83

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2. Chronological Bibliography 1933 Carman, L. A. The Efficacy of Tests. The American Accountant. December: 360-366. 1949 Robbers, Herbert and PiDnan, E. I. G. Application of the method of mixtures to quadratic forms in normal vanates. Annals of Mathematical Statistics, 20: 552-560. 195S Aitchison, I. On the Distribution of a Positive Random Vanable Having a Discrete Probability Mass at He Origin. Journal of the American Statistical Association, 50: 901-908. 1961 van Heerden, A. Steekproeven als Middel van Accountantscontrolex (Statistical Sampling as a Means of Auditing). Maandblad voor Accountancy en Bedri~shuishou~kunde, I-1 :453. 1962 Cyert, R. M. and Davidson, H. fusion. Statistical Sampl~ngfor Accounting Ir,formation. Englewood Cliffs, NI: Prentice-HaU, hnc. 1963 Stnnger, K. W. Practical Aspects of Statistical Sampling in Auditing. Proceedings of the Business and Economic Stans~cs Section, 405411, Washington, D.C.: American Statistical Association. 1972 Intemal Revenue Service. Audit Assessments Based on Statistical Samples (Memorandum to Assistant Commissioner from Chief Counsel). Washington, D.C.: IRS. Meikle, G. R. Statistical Sampling in an Audit Context. Toronto: Canadian Institute of Chartered Accountants. 84

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1973 Anderson, R. J. and Teitlebaum, A. D. Dollars t Sarnpl~ng. Canadian Chartered Accountant ( after 1973 this joumal became CA Magazine ), Apnl: 3~39. Kaplan, R. S. Stochastic Model for Auditing. Journal of Accounting Research, 11: 3846. Kaplan, R. S. Statistical Sampling in Auditing with Auxiliary [nfonnabon Estimators. Journal of Accounting Research, 11 :238-258. 1974 GooUfellow, I. L., Loebbecke, I.K. and Neter, J. Some Perspectives on CAV Sampling Plans. Part I, CA Magazine, October: 23-30; Part II, CA Magazine, November: 46-53. McRae, T. W. Statistical Sampling for Audit and Control. London: John Wiley. 197S Anderson, R. I. and Leslie, D. A. Discussion of Consideration in Choosing Statistical Sampling Procedures In Auditing. Journal of Accounting Research, 13 Supplement: 53-64. Internal Revenue Service. Audit Assessments Based on Statistical Samples - Supplemental Memorandum (March 6 Memorandum to Chief Counsel from Director, Refund Litigation Division & Acing Director, Tax Court Litigation Division). Washington, D. C.: IRS. Kaplan, R. Sample Size Computations for Dollar-Unit Sampling. Journal of Accounting Research, 13 Supplement: 126-133. Loebbecke, I. K. and Neter,]. Considerations in Choosing Statistical Sampling Procedures in Auditing, Journal of Accounting Research, 13 Supplement: 38-52. Neter, J., and L~oebbecke, I. Behavior of Major Statistical Estimators in Sampling Accounting Populations--An Empirical Study, New York: American Institute of Certified Public Accountants. 85

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Teitlebaum, A. D., Leslie, D. A. and Anderson, R. I. An Analysis of Recent Commentary on DoBar-Un~t Sampling In Auditing, McGill L7niversity Working Paper, March. 1976 Smith, T. M. F. Statistical Sampling for Accountants. London: Accountancy Age Books. 1977 Barman, A. Wi~in-Item Vanation: A Stochastic Approach to Audit Uncenamty. The Accounting Review, 52 :4S0~64. Deakin, E.B. Discussants Response to Computing Upper Error Limits in DolBar Unit Sampling. Frontiers of Auditing Research, edited by B.E. Cushing and J.~. K~gstad. Austin: Bureau of Business Research, The University of Texas at Austin: 195-201. Felix, W. L. Ir. and Gr~mlund, R. A. Sampling Model for Audit Tests of Composite Accounts. Journal of Accounting Research, 15: 2342. Fienberg, S. E., Neter, I. and Leitch, R. A. Estimating the Total Overstatement Error in Accounting Populations. Journal of the American Statistical Association, 72: 295-302. Garstka, S. J. Computing Upper Error Limits In DoBar-Un~t Sampling. Frontiers of Auditing Research, edited by B. E. Cushing and I. L. Krogstad. Austin: Bureau of Business Research, The University of Texas at Austin, 163-82. Garstka. S. I. Models for Computing Upper Error Limits in Dollar-Unit Sampling. Journal ofAccouniingResearch, 15:179-192. Leslie, D. A. Discussant's Response to Computing Upper Error Limits in Dollar Unit Sampling. Frontiers calf Auditing Research, edited by B. E. Cushing and I. L. Krogstad. Austin: Bureau of Business Research, The University of Texas at Austin: ~X3-91. Neter, I. and Loebbecke, I.K. On the Behavior of Statistical Estimators when Sampling Accounting Populations, Journal of the American Statistical Association, 72: 501-507. 86

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