Compara mètodes

	Regressió amb Elastic Net	Regressió quantílica
Camp	Estadística	Econometria
Família	Regression model	Regression model
Any d'origen	2005	1978
Autor original	Hui Zou and Trevor Hastie	Koenker & Bassett
Tipus	Penalized linear regression	Conditional quantile regression
Font seminal	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Àlies	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionats	6	5
Resum	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.