Applications of Subset Selection Procedures and Bayesian Ranking Methods in Analysis of Traffic Fatality Data

dc.contributor.author	McDonald, Gary C.
dc.date.accessioned	2017-09-06T16:31:15Z
dc.date.available	2017-09-06T16:31:15Z
dc.date.issued	2016-11
dc.description.abstract	Nonparametric 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.sponsorship	Kresge OA fund	en_US
dc.identifier.citation	WIREs Comput Stat 2016, 8:222–237. doi: 10.1002/wics.1385	en_US
dc.identifier.uri	http://hdl.handle.net/10323/4580
dc.language.iso	en_US	en_US
dc.publisher	WIREs Computational Statistics	en_US
dc.subject	Probability of a correct selection	en_US
dc.subject	Fatality analysis reporting system	en_US
dc.subject	Bayesian inference	en_US
dc.subject	WinBugs	en_US
dc.subject	Additive model	en_US
dc.subject	Tukey one-degree-of-freedom test for additivity	en_US
dc.title	Applications of Subset Selection Procedures and Bayesian Ranking Methods in Analysis of Traffic Fatality Data	en_US
dc.type	Article	en_US

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