Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия по методу наименьших усеченных квадратов (LTS)× | RANSAC-регрессия× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1984 | 1981 |
| Автор метода≠ | Peter J. Rousseeuw | Fischler & Bolles |
| Тип | Robust linear regression | Robust linear regression |
| Основополагающий источник≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. 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 ↗ |
| Другие названия≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. | 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. |
| ScholarGateНабор данных ↗ |
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