Approximations of choice probabilities in mixed logit models

Kalouptsidis, N; Psaraki, V

dc.contributor.author	Kalouptsidis, N	en
dc.contributor.author	Psaraki, V	en
dc.date.accessioned	2014-03-01T01:32:49Z
dc.date.available	2014-03-01T01:32:49Z
dc.date.issued	2010	en
dc.identifier.issn	0377-2217	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20228
dc.subject	Approximate choice probabilities	en
dc.subject	Discrete choice	en
dc.subject	Mixed logit	en
dc.subject	Random utility maximization models	en
dc.subject.classification	Management	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Approximate choice probabilities	en
dc.subject.other	Approximate computation	en
dc.subject.other	Apriori	en
dc.subject.other	Computational costs	en
dc.subject.other	Discrete choice	en
dc.subject.other	High order	en
dc.subject.other	Log-likelihood maximization	en
dc.subject.other	Logit functions	en
dc.subject.other	Mixed logit	en
dc.subject.other	Mixed logit models	en
dc.subject.other	Random coefficients	en
dc.subject.other	Random utility maximization models	en
dc.subject.other	Simulation data	en
dc.subject.other	Simulation-based method	en
dc.subject.other	Taylor expansions	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Probability	en
dc.subject.other	Simulators	en
dc.title	Approximations of choice probabilities in mixed logit models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ejor.2009.01.017	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ejor.2009.01.017	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	This paper is concerned with the approximate computation of choice probabilities in mixed logit models. The relevant approximations are based on the Taylor expansion of the classical logit function and on the high order moments of the random coefficients. The approximate choice probabilities and their derivatives are used in conjunction with log likelihood maximization for parameter estimation. The resulting method avoids the assumption of an apriori distribution for the random tastes. Moreover experiments with simulation data show that it compares well with the simulation based methods in terms of computational cost. (C) 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	European Journal of Operational Research	en
dc.identifier.doi	10.1016/j.ejor.2009.01.017	en
dc.identifier.isi	ISI:000270647100019	en
dc.identifier.volume	200	en
dc.identifier.issue	2	en
dc.identifier.spage	529	en
dc.identifier.epage	535	en