方法对比
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| 混合Logit模型× | 蒙特卡洛模拟× | |
|---|---|---|
| 领域≠ | 计量经济学 | 决策 |
| 方法族≠ | Regression model | MCDM |
| 起源年份≠ | 2000 | 1949 |
| 提出者≠ | Daniel McFadden & Kenneth Train | Metropolis, N., Ulam, S. |
| 类型≠ | Random-parameters discrete choice model | Robustness wrapper — Monte Carlo uncertainty propagation |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli | — |
| 相关≠ | 3 | 0 |
| 摘要≠ | 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. |
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