Compara mètodes

	Model Logit Mixt	Simulació Monte Carlo
Camp	Econometria	Presa de decisions
Família	Regression model	MCDM
Any d'origen	2000	1949
Autor original	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Tipus	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Font seminal	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Àlies	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	—
Relacionats	3	0
Resum	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.