Exact Distributions of Bayesian Cox-Snell Bounds in Auditing

Moors, J.J.A.; Janssens, Margo

doi:10.2307/2491211

Cited by 3 publications

(3 citation statements)

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“…Neter and Godfrey (1985) and Moors and Janssens(1987) study the applicability on audit populations. A funny aspect of the Cox-Snell model is that if it is right small items are as informative as large items, so it is no longer necessary to draw items with probabilities proportional to their sizes (the modern variant of MUS).…”

Section: Non Stationary Situationsmentioning

confidence: 99%

On randomization, modeling and experimental design; a new example in an old discussion

Vos

1991

Statistica Neerlandica

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Fisher and “Student” quarreled in the early days of statistics about the design of experiments, meant to measure the difference in yield between to breeds of corn. This discussion comes down to randomization versus model building. More than half a century has passed since, but the different views remain. In this paper the discussion is put in terms of artificial randomization and natural randomization, the latter being what remains after appropriate modeling. Also the Bayesian position is discussed. An example in terms of the old corn‐breeding discussion is given, showing that a simple robust model may lead to inference and experimental design that outperforms the inference from randomized experiments by far. Finally similar possibilities are suggested in statistical auditing.

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Section: Non Stationary Situationsmentioning

confidence: 99%

On randomization, modeling and experimental design; a new example in an old discussion

Vos

1991

Statistica Neerlandica

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“…Mixed models for the familiar auditing situation involving one infallible audi-tor have been discussed in the literature: Cox and Snell (1979) derived Bayesian estimators and upper limits for a model with non-negative errors with a probability mass in zero. Moors (1983) and Moors and Janssens (1989) expanded on this. Estimators for continuous, but not necessarily positive, errors with a point mass in zero were derived by Fienberg et al (1977), Tamura and Frost (1986), Tamura (1988) and Laws and O'Hagan (2000).…”

Section: Introductionmentioning

confidence: 96%

A Mixed Model for Double Checking Fallible Auditors

Raats

Moors

Genugten

2006

Communications in Statistics - Theory and Methods

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A Mixed Model for Double Checking Fallible AuditorsRaats, V.M.; Moors, J.J.A.; van der Genugten, B.B. AbstractThe paper discusses the problem of a fallible auditor who assesses the values of sampled records, but may make mistakes. To detect these mistakes, a subsample of the checked elements is checked again, now by an infallible expert.We propose a model for this kind of double check, which takes into account that records are often correct; however, if they are incorrect, the errors can take on many different values -as is often the case in audit practice. The model therefore involves error probabilities as well as distributional parameters for error sizes.We derive maximum likelihood estimators for these model parameters and derive from them an estimator for the mean size of the errors in the population. A simulation study shows that the latter outperforms some other -previously introduced -estimators.

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“…Moors (1983) and Moors and Janssens (1989) expanded on this. Estimators for continuous, but not necessarily positive, errors with a point mass in zero were derived by Fienberg et al (1977), Tamura and Frost (1986), Tamura (1988) and Laws and O'Hagan (2000).…”

Section: Introductionmentioning

confidence: 96%

A Mixed Model for Double Checking Fallible Auditors

Raats

Moors²,

Genugten

2004

SSRN Journal

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A Mixed Model for Double Checking Fallible AuditorsRaats, V.M.; Moors, J.J.A.; van der Genugten, Ben General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractThe paper discusses the problem of a fallible auditor who assesses the values of sampled records, but may make mistakes. To detect these mistakes, a subsample of the checked elements is checked again, now by an infallible expert.We propose a model for this kind of double check, which takes into account that records are often correct; however, if they are incorrect, the errors can take on many different values -as is often the case in audit practice. The model therefore involves error probabilities as well as distributional parameters for error sizes.We derive maximum likelihood estimators for these model parameters and derive from them an estimator for the mean size of the errors in the population. A simulation study shows that the latter outperforms some other -previously introduced -estimators.

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Exact Distributions of Bayesian Cox-Snell Bounds in Auditing

Cited by 3 publications

References 4 publications

On randomization, modeling and experimental design; a new example in an old discussion

On randomization, modeling and experimental design; a new example in an old discussion

A Mixed Model for Double Checking Fallible Auditors

A Mixed Model for Double Checking Fallible Auditors

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