Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Logit Mixto× | Simulación de Monte Carlo× | |
|---|---|---|
| Campo≠ | Econometría | Toma de decisiones |
| Familia≠ | Regression model | MCDM |
| Año de origen≠ | 2000 | 1949 |
| Autor original≠ | Daniel McFadden & Kenneth Train | Metropolis, N., Ulam, S. |
| Tipo≠ | Random-parameters discrete choice model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fuente 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 DOI ↗ |
| Alias≠ | Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli | — |
| Relacionados≠ | 3 | 0 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|