Mixed Logit
The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.
Source record
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- Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. · ISBN 978-0-521-74738-7
- McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470. · DOI 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
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