Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uthabiti wa MM kwa Regresi Imara× | Mbinu ya S-estimator kwa ajili ya Regresi Imara× | |
|---|---|---|
| Nyanja | Takwimu | Takwimu |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1987 | 1984 |
| Mwanzilishi≠ | Victor J. Yohai | Rousseeuw & Yohai (1984) |
| Aina | Robust linear regression | Robust linear regression |
| Chanzo asilia≠ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗ |
| Majina mbadala≠ | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | S-estimation, robust S-regression, S-Tahmin Edici |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. | The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator. |
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