Comparar métodos

	Modelo Logit Misto	Regressão Logística Multinomial	Modelo de Escolha Discreta Logit Aninhado
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	2000	1974	1985
Autor original	Daniel McFadden & Kenneth Train	McFadden	Daniel McFadden; Ben-Akiva & Lerman
Tipo	Random-parameters discrete choice model	Multinomial logistic regression	Discrete choice regression model
Fonte seminal	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0
Outros nomes	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli
Relacionados	3	5	3
Resumo	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	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.