Vertaile menetelmiä

	Elastic Net -regressio	Robust Regression
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2005	1964
Kehittäjä	Hui Zou and Trevor Hastie	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Tyyppi	Penalized linear regression	Regression with outlier resistance
Alkuperäislähde	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.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Rinnakkaisnimet	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Liittyvät	6	6
Tiivistelmä	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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.