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On approximate Monetary Unit Sampling


  • Carrizosa, Emilio


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.

Suggested Citation

  • Carrizosa, Emilio, 2012. "On approximate Monetary Unit Sampling," European Journal of Operational Research, Elsevier, vol. 217(2), pages 479-482.
  • Handle: RePEc:eee:ejores:v:217:y:2012:i:2:p:479-482 DOI: 10.1016/j.ejor.2011.09.037

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    References listed on IDEAS

    1. repec:spr:compst:v:64:y:2006:i:2:p:271-284 is not listed on IDEAS
    2. Carrizosa, Emilio, 2010. "Unequal probability sampling from a finite population: A multicriteria approach," European Journal of Operational Research, Elsevier, vol. 201(2), pages 500-504, March.
    3. S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
    4. Frank Dietrich & Hartmut Kliemt & Michael Imhoff, 2002. "Introduction," Homo Oeconomicus, Institute of SocioEconomics, vol. 19, pages 7-8.
    5. repec:spr:infosf:v:4:y:2002:i:4:d:10.1023_a:1020831608657 is not listed on IDEAS
    6. R. Blanquero & E. Carrizosa & E. Conde, 2006. "Inferring Efficient Weights from Pairwise Comparison Matrices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(2), pages 271-284, October.
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    Cited by:

    1. Laitinen, Erkki K. & Laitinen, Teija, 2015. "A probability tree model of audit quality," European Journal of Operational Research, Elsevier, vol. 243(2), pages 665-677.


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