Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия по методу наименьших усеченных квадратов (LTS)× | M-оценки (робастная регрессия)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1984 | 2009 |
| Автор метода≠ | Peter J. Rousseeuw | Peter J. Huber |
| 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 ↗ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ |
| Другие названия≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler |
| 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. | 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. |
| ScholarGateНабор данных ↗ |
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