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dc.contributor.authorMcDonald, Gary C.
dc.date.accessioned2017-09-06T16:31:15Z
dc.date.available2017-09-06T16:31:15Z
dc.date.issued2016-11
dc.identifier.citationWIREs Comput Stat 2016, 8:222–237. doi: 10.1002/wics.1385en_US
dc.identifier.urihttp://hdl.handle.net/10323/4580
dc.description.abstractNonparametric and parametric subset selection procedures are used in the analysis of state motor vehicle traffic fatality rates (MVTFRs), for the years 1994 through 2012, to identify subsets of states that contain the ‘best’ (lowest MVTFR) and ‘worst’ (highest MVTFR) states with a prescribed probability. A new Bayesian model is developed and applied to the traffic fatality data and the results contrasted to those obtained with the subset selection procedures. All analyses are applied within the context of a two-way block design.en_US
dc.description.sponsorshipKresge OA funden_US
dc.language.isoen_USen_US
dc.publisherWIREs Computational Statisticsen_US
dc.subjectprobability of a correct selectionen_US
dc.subjectfatality analysis reporting systemen_US
dc.subjectbayesian inferenceen_US
dc.subjectWinBugsen_US
dc.subjectadditive modelen_US
dc.subjectTukey one-degree-of-freedom test for additivityen_US
dc.titleApplications of Subset Selection Procedures and Bayesian Ranking Methods in Analysis of Traffic Fatality Dataen_US
dc.typeArticleen_US


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