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dc.contributor.authorDadson, Kwabena
dc.description.abstractThe problem of outliers is an old phenomenon in statistics, and it appears with surprising frequency in many datasets in both the natural and social sciences and can have both positive and negative effects on statistical analysis. Unlike the traditional approach to dealing with outliers in a dataset, this study considers both the base and contaminating distributions that generate outliers and estimates the best-fitting distribution for each separately. Using the natural conjugate prior distribution for the probability of occurrence, the ‘Bayesian averaging’ technique is used in a way that preserves most of the information in the total dataset. The KS-test and AD-test statistics were computed by contrasting the simulated to the actual data distribution to obtain the comparative metric. Analysis of seven sample datasets (each containing outliers) indicated that these alternate simulation procedures provided a stronger goodness-of-fit to the historical data when compared to other, more traditional approaches.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleImproved Monte Carlo Simulation in the Presence of Outliers Using Labeling and Bayesian Averagingen_US
dc.typeThesisen_US
dc.date.accessioned2024-10-31T19:01:04Z
dc.date.available2024-10-31T19:01:04Z
dc.date.issued2024-07
dc.identifier.urihttps://hdl.handle.net/10365/34019
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeBusinessen_US
ndsu.departmentTransportation and Logisticsen_US
ndsu.advisorBullock, David


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