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| Minimi Quadrati Pesati Bayesiani (Bayesian WLS)× | Minimi Quadrati Pesati Robusti (Robust WLS)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1971 | 1964/1981 |
| Ideatore≠ | Arnold Zellner (Bayesian econometrics framework) | Huber, P. J. |
| Tipo≠ | Bayesian weighted regression | Robust weighted regression |
| Fonte seminale≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. ISBN: 978-0471169376 | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 |
| Alias | Bayesian weighted regression, BWLS, Bayesian heteroscedastic regression, weighted Bayesian linear regression | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. | Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates. |
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