Comparer des méthodes

	Modèle Logit Mixte	Modèle de choix discrets Logit Hiérarchique
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000	1985
Auteur d'origine	Daniel McFadden & Kenneth Train	Daniel McFadden; Ben-Akiva & Lerman
Type	Random-parameters discrete choice model	Discrete choice regression model
Source fondatrice	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0
Alias	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli
Apparentées	3	3
Résumé	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.	The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.