Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustā Rīdža regresija× | Regresija ar elastīgo tīklu× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 2005 |
| Autors≠ | Silvapulle (1991); building on Tikhonov (1963) and Huber (1964) | Hui Zou and Trevor Hastie |
| Tips≠ | Regularized robust linear regression | Penalized linear regression |
| Pirmavots≠ | Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333. link ↗ | Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. DOI ↗ |
| Citi nosaukumi | ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero. | Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone. |
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