On approximate Monetary Unit Sampling

Monetary Unit Sampling (MUS), also known as Dollar-Unit Sampling, is a popular sampling strategy in Auditing, in which all units are to be randomly selected with probabilities proportional to the book value. However, if units sizes have very large variability, no vector of probabilities exists fulfi...

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Author: Carrizosa Priego, Emilio José
Format: article
Status:Published version
Publication Date:2011
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/107645
Online Access:https://hdl.handle.net/11441/107645
https://doi.org/10.1016/j.ejor.2011.09.037
Access Level:Open access
Keyword:Nonlinear programming
Monetary Unit Sampling
Statistical sampling
Karush–Kuhn–Tucker conditions
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repository_id_str
spelling On approximate Monetary Unit SamplingCarrizosa Priego, Emilio JoséNonlinear programmingMonetary Unit SamplingStatistical samplingKarush–Kuhn–Tucker conditionsMonetary Unit Sampling (MUS), also known as Dollar-Unit Sampling, is a popular sampling strategy in Auditing, in which all units are to be randomly selected with probabilities proportional to the book value. However, if units sizes have very large variability, no vector of probabilities exists fulfilling the requirement that all probabilities are proportional to the associated book values. In this note we propose a Mathematical Optimization approach to address this issue. An optimization program is posed, structural properties of the optimal solution are analyzed, and an algorithm yielding the optimal solution in time and space linear to the number of population units is given.ELSEVIER SCIENCE BVEstadística e Investigación OperativaFQM329: Optimización2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/107645https://doi.org/10.1016/j.ejor.2011.09.037reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEuropean Journal of Operational Research, 217 (2), 479-482.http://doi.org/10.1016/j.ejor.2011.09.037info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1076452026-06-17T12:51:07Z
dc.title.none.fl_str_mv On approximate Monetary Unit Sampling
title On approximate Monetary Unit Sampling
spellingShingle On approximate Monetary Unit Sampling
Carrizosa Priego, Emilio José
Nonlinear programming
Monetary Unit Sampling
Statistical sampling
Karush–Kuhn–Tucker conditions
title_short On approximate Monetary Unit Sampling
title_full On approximate Monetary Unit Sampling
title_fullStr On approximate Monetary Unit Sampling
title_full_unstemmed On approximate Monetary Unit Sampling
title_sort On approximate Monetary Unit Sampling
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
author_role author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimización
dc.subject.none.fl_str_mv Nonlinear programming
Monetary Unit Sampling
Statistical sampling
Karush–Kuhn–Tucker conditions
topic Nonlinear programming
Monetary Unit Sampling
Statistical sampling
Karush–Kuhn–Tucker conditions
description Monetary Unit Sampling (MUS), also known as Dollar-Unit Sampling, is a popular sampling strategy in Auditing, in which all units are to be randomly selected with probabilities proportional to the book value. However, if units sizes have very large variability, no vector of probabilities exists fulfilling the requirement that all probabilities are proportional to the associated book values. In this note we propose a Mathematical Optimization approach to address this issue. An optimization program is posed, structural properties of the optimal solution are analyzed, and an algorithm yielding the optimal solution in time and space linear to the number of population units is given.
publishDate 2011
dc.date.none.fl_str_mv 2011
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/107645
https://doi.org/10.1016/j.ejor.2011.09.037
url https://hdl.handle.net/11441/107645
https://doi.org/10.1016/j.ejor.2011.09.037
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv European Journal of Operational Research, 217 (2), 479-482.
http://doi.org/10.1016/j.ejor.2011.09.037
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv ELSEVIER SCIENCE BV
publisher.none.fl_str_mv ELSEVIER SCIENCE BV
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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