Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule

Kalouptsidis, N; Koutroumbas, K; Psaraki, V

dc.contributor.author	Kalouptsidis, N	en
dc.contributor.author	Koutroumbas, K	en
dc.contributor.author	Psaraki, V	en
dc.date.accessioned	2014-03-01T01:26:01Z
dc.date.available	2014-03-01T01:26:01Z
dc.date.issued	2007	en
dc.identifier.issn	0377-2217	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17881
dc.subject	Decision analysis	en
dc.subject	Discrete choice models	en
dc.subject	Logit model	en
dc.subject	Pattern recognition	en
dc.subject	Random utility model	en
dc.subject.classification	Management	en
dc.subject.classification	Operations Research & Management Science	en
dc.subject.other	Data reduction	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Vectors	en
dc.subject.other	Weibull distribution	en
dc.subject.other	Discrete choice models	en
dc.subject.other	Logit model	en
dc.subject.other	Random utility model	en
dc.subject.other	Decision theory	en
dc.title	Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ejor.2005.11.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ejor.2005.11.004	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this paper random utility maximization based on maximization of correct classification of the choice decisions over a given data set is considered. It is shown that if the disturbance vector in the random utility model is independent and identically distributed, then preference determination based on the most probable alternative reduces to deterministic utility maximization. As a consequence of the above equivalence, the form of the error distribution (normal, Weibull, uniform etc.) plays no role in the determination of the preferred alternative. Parameter estimation under the most probable alternative rule is carried out using two methods. The first is based on the solution of an appropriately defined system of linear inequalities and the second one is based on the function optimization of a newly proposed function, whose optimum is achieved when the number of correctly classified individuals is maximized. The ability to use these algorithms in the framework of pattern recognition and machine learning is pointed out. Simulations and a real case study involving intercity travel behavior are employed to assess the proposed methods. (c) 2005 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.2005.11.004	en
dc.identifier.isi	ISI:000242102800031	en
dc.identifier.volume	176	en
dc.identifier.issue	3	en
dc.identifier.spage	1778	en
dc.identifier.epage	1794	en