Compară metode

	Modelul Logit Mixt	Bayesian Regression	Regresie logistică multinomială	Modelul Logit Ierarhizat (Nested Logit)
Domeniu	Econometrie	Bayesian	Econometrie	Econometrie
Familie	Regression model	Bayesian methods	Regression model	Regression model
Anul apariției	2000	—	1974	1985
Autorul original	Daniel McFadden & Kenneth Train	—	McFadden	Daniel McFadden; Ben-Akiva & Lerman
Tip	Random-parameters discrete choice model	Bayesian linear model	Multinomial logistic regression	Discrete choice regression model
Sursa seminală	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	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
Denumiri alternative	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	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
Înrudite	3	2	5	3
Rezumat	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.