TY  - JOUR
PY  - 2006//
TI  - Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
JO  - Transportation research record
A1  - Ma, Junlai
A1  - Kockelman, Kara Maria
SP  - 24
EP  - 34
VL  - 1950
IS  - 
N2  - 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.<p />
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/
ID  - ref1
ER  -