Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| MM-оценка для робастной регрессии× | Регрессия по методу наименьших медиан квадратов (LMS)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1987 | 1984 |
| Автор метода≠ | Victor J. Yohai | Peter J. Rousseeuw |
| Тип | Robust linear regression | Robust linear regression |
| Основополагающий источник≠ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Другие названия≠ | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | LMS, least median of squares regression, en küçük medyan kareler (LMS) |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers. |
| ScholarGateНабор данных ↗ |
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