Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| M-оценки (робастная регрессия)× | MM-оценка для робастной регрессии× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2009 | 1987 |
| Автор метода≠ | Peter J. Huber | Victor J. Yohai |
| Тип | Robust linear regression | Robust linear regression |
| Основополагающий источник≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Другие названия | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Связанные | 5 | 5 |
| Сводка≠ | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit. | 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. |
| ScholarGateНабор данных ↗ |
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