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| Gemischtes Logit-Modell× | Bayes'sche Regression× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Bayes-Statistik |
| Familie≠ | Regression model | Bayesian methods |
| Entstehungsjahr≠ | 2000 | — |
| Urheber≠ | Daniel McFadden & Kenneth Train | — |
| Typ≠ | Random-parameters discrete choice model | Bayesian linear model |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen≠ | Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Verwandt≠ | 3 | 2 |
| Zusammenfassung≠ | 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. |
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