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dc.contributor.authorAndersson, Jonas
dc.contributor.authorOlden, Andreas
dc.contributor.authorRusina, Aija
dc.date.accessioned2021-01-04T10:00:25Z
dc.date.available2021-01-04T10:00:25Z
dc.date.issued2020-12-31
dc.identifier.issn1500-4066
dc.identifier.urihttps://hdl.handle.net/11250/2721233
dc.description.abstractIn this paper we investigate the EM-estimator of the model by Caudill et al. (2005). The purpose of the model is to identify items, e.g. individuals or companies, that are wrongly classified as honest; an example of this is the detection of tax evasion. Normally, we observe two groups of items, labeled fraudulent and honest, but suspect that many of the observationally honest items are, in fact, fraudulent. The items observed as honest are therefore divided into two unobserved groups, honestH, representing the truly honest, and honestF, representing the items that are observed as honest, but that are actually fraudulent. By using a multinomial logit model and assuming commonality between the observed fraudulent and the unobserved honestF, Caudill et al. (2005) present a method that uses the EM-algorithm to separate them. By means of a Monte Carlo study, we investigate how well the method performs, and under what circumstances. We also study how well bootstrapped standard errors estimates the standard deviation of the parameter estimators.en_US
dc.language.isoengen_US
dc.publisherFORen_US
dc.relation.ispartofseriesDiscussion paper;15/20
dc.subjectFraud detectionen_US
dc.subjectEM-algorithmen_US
dc.subjectmultinomial logit modelen_US
dc.subjectMonte Carlo studyen_US
dc.titleFraud detection by a multinomial model: Separating honesty from unobserved frauden_US
dc.typeWorking paperen_US
dc.source.pagenumber15en_US


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