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| MM-Schätzung für robuste Regression× | RANSAC-Regression× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1987 | 1981 |
| Urheber≠ | Victor J. Yohai | Fischler & Bolles |
| Typ | Robust linear regression | Robust linear regression |
| Wegweisende Quelle≠ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395. DOI ↗ |
| Aliasnamen | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points. |
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