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| MM-regresia pre robustnú regresiu× | Regresia metódou najmenších štvorcov (OLS)× | |
|---|---|---|
| Odbor≠ | Štatistika | Ekonometria |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1987 | 2019 |
| Tvorca≠ | Victor J. Yohai | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robust linear regression | Linear regression |
| Pôvodný zdroj≠ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Ďalšie názvy | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. | 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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