@article{ref1,
title="Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity",
journal="Transportation research record",
year="2006",
author="Ma, Junlai and Kockelman, Kara Maria",
volume="1950",
number="",
pages="24-34",
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.<p />",
language="en",
issn="0361-1981",
doi="",
url="http://dx.doi.org/"
}