Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Стійка множинна лінійна регресія× | Гребенева регресія× | |
|---|---|---|
| Галузь≠ | Статистика | Машинне навчання |
| Родина≠ | Regression model | Machine learning |
| Рік появи≠ | 1964–1980s | 1970 |
| Автор методу≠ | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Robust linear regression | L2-regularized linear regression |
| Основоположне джерело≠ | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Інші назви | robust MLR, M-estimator regression, resistant multiple regression, robust OLS | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
| ScholarGateНабір даних ↗ |
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