Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Anggaran MM untuk Regresi Teguh× | Regresi RANSAC× | |
|---|---|---|
| Bidang | Statistik | Statistik |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1987 | 1981 |
| Pengasas≠ | Victor J. Yohai | Fischler & Bolles |
| Jenis | Robust linear regression | Robust linear regression |
| Sumber perintis≠ | 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 ↗ |
| Alias | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
|
|