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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: 901908.
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~. DolUarunit Sampling.
Canadian Chartered Accountant (after 1973, this publication became CA
Magazine ), April: 3039.
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: 5364.
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The discussion focuses partly on the paper by Loebbecke and
Neter and partly on tile ~enfor~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 doBarun~t sampling with He
Stnnger bound, rather than the combined attributesvanables
bound because He Stnnger bound is less conservative and more
widely used. The discussants believe mat doBarunit sampling
wid1 He Stnnger boulKl is appropnae in almost all circumstances
so that He auditor need not consider altemative sampling
approaches and fallback 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. WithinItem 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
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meanperun~t and difference estimators.
Beck, P. I. (1980~. A Cntical Analysis of the Regression Estimator in
Audit Sampling. Journal of Accounting Research, IS: 1637.
This study supplements the Neter and Toebbecke 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: 779781, 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 largesample
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: 360366.
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: 124132.
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: 19S201.
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 dollarunit 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 :4267.
The power characteristics of eight test statistics, used with both
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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
:218241.
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 QuasiBayesian and Moments Bounds. The
Accounting Review, 61 :3657.
This article reports the results of comparing the performance of
the moments bound with that of McCray's quasiBayesian 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: 92103.
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
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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 "bemnomlal" 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 betano~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: 103107.
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 :295302.
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
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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: 103120.
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: 5775.
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 #287, 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
DoDarUrut Sampling. Journal of Accounang Research, 15 :17992.
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 DoBarUn~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: 16382.
This paper poses a number of models to be used in conjunction
win doBarun~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: 103113.
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
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Proportional to Size of Book Values, Journal of Accounting Research,
17: 2359.
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: 497525.
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:
9330; Part II, CA Magazine, November: 4653.
Combined attr~bueesvanables (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
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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 nondollar audit units, ineffective
design for disclosing errors, inadequacy of onesided 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 DollarUn~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 nonzero 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 .
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Hylas, R. E. arid Ashton, R. H. (1982). Audit Detection of Financial
Statement Erwrs. The Accounting Review, 57: 751765.
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 :270293.
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
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128.
This work compams We long run performance of venous
Bayesian and nonBayesian 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 :267280.
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 AudinngThe State of
He Art Annual Accounting Review, 1:~ ~ 3127.
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
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practice and calls for msearch to develop more efficient bounds.
Tamura, H. (1985). Analysis of He GarstkaOhison Bounds.
Auditing: A Journal of Practice & Theory, 4: 133142.
This article comments on a property of He Gars~aOhison
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:
364371.
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 DoldarUnit Sampling in
Auditing, McGill University working paper, March.
This paper is a response to tile twopart 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 dollarunit sampling, the handling of over and
understatement errors, the method of sample selection, He
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evaluation of an upper bound, and a compan son of monetary t
sampling with the Stringer bound and lineitem 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 :7696.
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.
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2. Chronological Bibliography
1933
Carman, L. A. The Efficacy of Tests. The American Accountant.
December: 360366.
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: 552560.
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: 901908.
1961
van Heerden, A. Steekproeven als Middel van Accountantscontrolex
(Statistical Sampling as a Means of Auditing). Maandblad voor
Accountancy en Bedri~shuishou~kunde, I1 :453.
1962
Cyert, R. M. and Davidson, H. fusion. Statistical Sampl~ngfor Accounting
Ir,formation. Englewood Cliffs, NI: PrenticeHaU, 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:
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