Premium
Applications of subset selection procedures and Bayesian ranking methods in analysis of traffic fatality data
Author(s) -
McDonald Gary C.
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1385
Subject(s) - bayesian probability , nonparametric statistics , context (archaeology) , selection (genetic algorithm) , ranking (information retrieval) , model selection , computer science , data mining , statistics , mathematics , machine learning , geography , archaeology
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. WIREs Comput Stat 2016, 8:222‐237. doi: 10.1002/wics.1385 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis > Nonparametric Methods