Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Robust simple linear regression× | Robustна множествена линейна регресия× | |
|---|---|---|
| Област | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1964-1987 | 1964–1980s |
| Създател≠ | Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987) | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Тип | Robust linear regression | Robust linear regression |
| Основополагащ източник≠ | Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339 | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Други названия | robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Свързани | 6 | 6 |
| Резюме≠ | Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret. | Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients. |
| ScholarGateНабор от данни ↗ |
|
|