Bayesian multinomial logit

Washington, S; Congdon, P; Karlaftis, MG; Mannering, FL

dc.contributor.author	Washington, S	en
dc.contributor.author	Congdon, P	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Mannering, FL	en
dc.date.accessioned	2014-03-01T01:29:54Z
dc.date.available	2014-03-01T01:29:54Z
dc.date.issued	2009	en
dc.identifier.issn	03611981	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19401
dc.subject	Multinomial Logit	en
dc.subject.other	Bayesian	en
dc.subject.other	Bayesian approaches	en
dc.subject.other	Bayesian frameworks	en
dc.subject.other	Bayesian inference	en
dc.subject.other	Covariates	en
dc.subject.other	Crash outcomes	en
dc.subject.other	Discrete choice models	en
dc.subject.other	Gibbs samplers	en
dc.subject.other	Markov chain Monte Carlo	en
dc.subject.other	Model choice	en
dc.subject.other	Model specifications	en
dc.subject.other	Multinomial Logit	en
dc.subject.other	Prior information	en
dc.subject.other	Random parameter model	en
dc.subject.other	Route choice	en
dc.subject.other	Statistical models	en
dc.subject.other	Transportation research	en
dc.subject.other	Estimation	en
dc.subject.other	Inference engines	en
dc.subject.other	Markov processes	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Research	en
dc.subject.other	Bayesian networks	en
dc.title	Bayesian multinomial logit	en
heal.type	journalArticle	en
heal.identifier.primary	10.3141/2136-04	en
heal.identifier.secondary	http://dx.doi.org/10.3141/2136-04	en
heal.publicationDate	2009	en
heal.abstract	Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting IIA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile.	en
heal.journalName	Transportation Research Record	en
dc.identifier.doi	10.3141/2136-04	en
dc.identifier.issue	2136	en
dc.identifier.spage	28	en
dc.identifier.epage	36	en