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Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model

  • William H. Greene
  • David A. Hensher

Latent class models offer an alternative perspective to the popular mixed logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed, although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class model is a random parameter latent class model which allows for another layer of preference heterogeneity within each class. This article sets out the random parameter latent class model and illustrates its applications using a stated choice data set on alternative freight distribution attribute packages pivoted around a recent trip in Australia.

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File URL: http://hdl.handle.net/10.1080/00036846.2011.650325
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Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 45 (2013)
Issue (Month): 14 (May)
Pages: 1897-1902

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Handle: RePEc:taf:applec:45:y:2013:i:14:p:1897-1902
DOI: 10.1080/00036846.2011.650325
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  1. Hensher, David A. & Puckett, Sean M. & Rose, John M., 2007. "Agency decision making in freight distribution chains: Establishing a parsimonious empirical framework from alternative behavioural structures," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 924-949, November.
  2. Sean Puckett & David Hensher & John Rose & Andrew Collins, 2007. "Design and development of a stated choice experiment for interdependent agents: accounting for interactions between buyers and sellers of urban freight services," Transportation, Springer, vol. 34(4), pages 429-451, July.
  3. Angel Bujosa Bestard & Antoni Riera Font & Robert L. Hicks, 2009. "Combining discrete and continuous representations of preference heterogeneity: a latent class approach," CRE Working Papers (Documents de treball del CRE) 2009/2, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
  4. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2.
  5. Everitt, B. S., 1988. "A finite mixture model for the clustering of mixed-mode data," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 305-309, April.
  6. Rose, John M. & Bliemer, Michiel C.J. & Hensher, David A. & Collins, Andrew T., 2008. "Designing efficient stated choice experiments in the presence of reference alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 395-406, May.
  7. Danielis, Romeo & Marcucci, Edoardo & Rotaris, Lucia, 2005. "Logistics managers' stated preferences for freight service attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(3), pages 201-215, May.
  8. Cullinane, Kevin & Toy, Neal, 2000. "Identifying influential attributes in freight route/mode choice decisions: a content analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(1), pages 41-53, March.
  9. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
  10. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
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