Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Оценщик Тейля-Сена× | Регрессия по методу наименьших усеченных квадратов (LTS)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1968 | 1984 |
| Автор метода≠ | Henri Theil (1950); P. K. Sen (1968) | Peter J. Rousseeuw |
| Тип | Robust linear regression | Robust linear regression |
| Основополагающий источник≠ | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| Другие названия | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. | 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. |
| ScholarGateНабор данных ↗ |
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