Fitting nonparametric mixed logit models via expectation-maximization algorithm

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dc.creatorPacifico, Daniele
dc.date2017-04-01T19:26:01Z
dc.date.accessioned2026-07-09T09:30:15Z
dc.descriptionIn this article, I provide an illustrative, step-by-step implementation of the expectation–maximization algorithm for the nonparametric estimation of mixed logit models. In particular, the proposed routine allows users to fit straight-forwardly latent-class logit models with an increasing number of mass points so as to approximate the unobserved structure of the mixing distribution.
dc.identifierOther:st0258
dc.identifierdoi:10.22004/ag.econ.208009
dc.identifierhttps://ageconsearch.umn.edu/record/208009/files/sjart_st0258.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/208009
dc.identifier.urihttp://hdl.handle.net/123456789/609580
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/208009
dc.titleFitting nonparametric mixed logit models via expectation-maximization algorithm
dc.typeText

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