Mixed Logit with Bounded Distributions of Correlated Partworths

  • Kenneth Train
  • Garrett Sonnier
Part of the The Economics of Non-Market Goods and Resources book series (ENGO, volume 6)


The use of a joint normal distribution for partworths is computationally attractive, particularly with Bayesian MCMC procedures, and yet is unrealistic for any attribute whose partworth is logically bounded (e.g., is necessarily positive or cannot be unboundedly large). A mixed logit is specified with partworths that are transformations of normally distributed terms, where the transformation induces bounds; examples include censored normals, log-normals, and S B distributions which are bounded on both sides. The model retains the computational advantages of joint normals while providing greater flexibility for the distributions of correlated partworths. The method is applied to data on customers’ choice among vehicles in stated choice experiments. The flexibility that the transformations allow is found to greatly improve the model, both in terms of fit and plausibility, without appreciably increasing the computational burden.


Mixed logit correlated partworths bounded distributions choice of vehicle 


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Copyright information

© Springer 2005

Authors and Affiliations

  • Kenneth Train
    • 1
  • Garrett Sonnier
    • 2
  1. 1.Department of EconomicsUniversity of CaliforniaBerkeley
  2. 2.Anderson School of ManagementUniversity of CaliforniaLos AngelesUSA

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