Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/66792
Authors: 
Train, Kenneth E.
Year of Publication: 
2008
Citation: 
[Journal:] Journal of Choice Modelling [ISSN:] 1755-5345 [Volume:] 1 [Year:] 2008 [Issue:] 1 [Pages:] 40-69
Abstract: 
This paper describes and implements three computationally attractive procedures for nonparametric estimation of mixing distributions in discrete choice models. The procedures are specic types of the well known EM (Expectation-Maximization) algorithm based on three dierent ways of approximating the mixing distribution nonparametrically: (1) a discrete distribution with mass points and frequencies treated as parameters, (2) a discrete mixture of continuous distributions, with the moments and weight for each distribution treated as parameters, and (3) a discrete distribution with fixed mass points whose frequencies are treated as parameters. The methods are illustrated with a mixed logit model of households' choices among alternative-fueled vehicles.
Subjects: 
mixed logit
probit
random coecients
EM algorithm
nonparametric estimation
Creative Commons License: 
http://creativecommons.org/licenses/by-nc/2.0/uk/
Document Type: 
Article
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