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| Bayesian Weighted Least Squares (Bayesian WLS)× | Bayesian OLS (Bayesian Ordinary Least Squares Regression)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr | 1971 | 1971 |
| Urheber≠ | Arnold Zellner (Bayesian econometrics framework) | Arnold Zellner |
| Typ≠ | Bayesian weighted regression | Bayesian linear regression |
| Wegweisende Quelle≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. ISBN: 978-0471169376 | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 |
| Aliasnamen | Bayesian weighted regression, BWLS, Bayesian heteroscedastic regression, weighted Bayesian linear regression | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Bayesian Weighted Least Squares combines the classical WLS weighting scheme — which downweights observations with high error variance — with Bayesian prior distributions over the regression coefficients and error variance. The result is a posterior distribution that reflects both the data likelihood and prior beliefs, providing full uncertainty quantification in heteroscedastic settings. | Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small. |
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