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dc.contributor.authorMartel-Escobar, María del Carmen
dc.contributor.authorVázquez-Polo, Francisco-José
dc.contributor.authorHernández-Bastida, Agustín
dc.identifier.citationMartel-Escobar, Mª.C.; Vázquez-Polo, F.J.; Hernández-Bastida, A. Bayesian Inference in Auditing with Partial Prior Information Using Maximum Entropy Priors. Entropy 2018, 20, 919; doi:10.3390/e20120919.es_ES
dc.description.abstractProblems in statistical auditing are usually one–sided. In fact, the main interest for auditors is to determine the quantiles of the total amount of error, and then to compare these quantiles with a given materiality fixed by the auditor, so that the accounting statement can be accepted or rejected. Dollar unit sampling (DUS) is a useful procedure to collect sample information, whereby items are chosen with a probability proportional to book amounts and in which the relevant error amount distribution is the distribution of the taints weighted by the book value. The likelihood induced by DUS refers to a 201–variate parameter p but the prior information is in a subparameter q linear function of p, representing the total amount of error. This means that partial prior information must be processed. In this paper, two main proposals are made: (1) to modify the likelihood, to make it compatible with prior information and thus obtain a Bayesian analysis for hypotheses to be tested; (2) to use a maximum entropy prior to incorporate limited auditor information. To achieve these goals, we obtain a modified likelihood function inspired by the induced likelihood described by Zehna (1966) and then adapt the Bayes’ theorem to this likelihood in order to derive a posterior distribution for q. This approach shows that the DUS methodology can be justified as a natural method of processing partial prior information in auditing and that a Bayesian analysis can be performed even when prior information is only available for a subparameter of the model. Finally, some numerical examples are presented.es_ES
dc.description.sponsorshipThis research was partially funded by MINECO, Spain (grant number EC02017–85577–P).es_ES
dc.rightsAtribución 3.0 España*
dc.subjectAuditing es_ES
dc.subjectBayesian inferencees_ES
dc.subjectDollar unit samplinges_ES
dc.subjectModified likelihoodes_ES
dc.subjectPartial prior informationes_ES
dc.titleBayesian Inference in Auditing with Partial Prior Information Using Maximum Entropy Priorses_ES

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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España