Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| RANSAC regresija× | Robustu kovariācijas novērtēšana (MCD)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1981 | 1999 |
| Autors≠ | Fischler & Bolles | Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD) |
| Tips≠ | Robust linear regression | Robust multivariate location-scatter estimator |
| Pirmavots≠ | 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 ↗ | Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223. DOI ↗ |
| Citi nosaukumi | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu | minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD) |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation. |
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