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Title: Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
Accession Number: 01020890
Record Type: Component
Record URL: Availability: Transportation Research Board Business Office 500 Fifth Street, NW Find a library where document is available Abstract: In practice, crash and injury counts are modeled by using a single equation or a series of independently specified equations, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to biases in sample databases. This paper offers a multivariate Poisson specification that simultaneously models injuries by severity. Parameter estimation is performed within the Bayesian paradigm with a Gibbs sampler for crashes on Washington State highways. Parameter estimates and goodness-of-fit measures are compared with a series of independent Poisson equations, and a cost–benefit analysis of a 10-mph speed limit change is provided as an example application.
Monograph Title: Monograph Accession #: 01030706
Language: English
Authors: Ma, JianmingKockelman, Kara MPagination: 24p
Publication Date: 2006
ISBN: 0309099595
Media Type: Print
Features: Figures
(1)
; References
(64)
; Tables
(9)
TRT Terms: Uncontrolled Terms: Geographic Terms: Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I81: Accident Statistics
Files: TRIS, TRB
Created Date: Mar 3 2006 10:49AM
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