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| Regressione di Huber× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1964 | 2019 |
| Ideatore≠ | Peter J. Huber | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Robust linear regression (M-estimation) | Linear regression |
| Fonte seminale≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati | 5 | 5 |
| Sintesi≠ | Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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