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| Regressione Robusta× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1964 | 2019 |
| Ideatore≠ | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Regression with outlier resistance | Linear regression |
| Fonte seminale≠ | Huber, P. J. (1964). Robust estimation of a location parameter. The 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 | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. | 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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