Hybrid choice models : Progress and challenges
Moshe Ben-Akiva,Daniel McFadden,Kenneth Train,Joan L. Walker,Chandra R. Bhat,Michel Bierlaire,Denis Bolduc,Axel Boersch-Supan,David Brownstone,David S. Bunch,Andrew Daly,André de Palma,Dinesh Gopinath,Anders Karlström,Marcela Munizaga +14 more
TLDR
In this paper, the authors discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation and incorporate several elements of cognitive process that have been identified as important to the choice process.Abstract:
We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.read more
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Multiple Imputation for Nonresponse in Surveys
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
BIOGEME: a free package for the estimation of discrete choice models
TL;DR: Reference TRANSP-OR-CONF-2006-048 URL: http://transp-or.epfl.ch/documents/proceedings/Bier03.pdf
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References
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Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
Journal ArticleDOI
Multiple Imputation for Nonresponse in Surveys.
C. D. Kershaw,Donald B. Rubin +1 more
TL;DR: This work focuses on the development of Imputation Models for Social Security Benefit Reconciliation in the context of a Finite Population and examines the role of Bayesian and Randomization--Based Inferences in these models.
Journal ArticleDOI
Mixed mnl models for discrete response
Daniel McFadden,Kenneth Train +1 more
TL;DR: In this article, the adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately definedartificial variables, and a practicalestimation of aarametricmixingfamily can be run by MaximumSimulated Likelihood EstimationorMethod ofSimulatedMoments, andeasilycomputedinstruments are provided that make the latter procedure fairly eAcient.
Journal ArticleDOI
Multiple Imputation for Nonresponse in Surveys
TL;DR: It is concluded that multiple Imputation for Nonresponse in Surveys should be considered as a legitimate method for answering the question of why people do not respond to survey questions.
Journal ArticleDOI
Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model
TL;DR: In this article, a quasi-random sequence for the estimation of the mixed multinomial logit model was proposed, which accommodates general patterns of competitiveness as well as heterogeneity across individuals in sensitivity to exogenous variables.