Comparer des méthodes

	Régression LASSO Bayésienne	Régression Elastic Net
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2008	2005
Auteur d'origine	Park & Casella	Hui Zou and Trevor Hastie
Type	Bayesian regularized regression	Penalized linear regression
Source fondatrice	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	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.
Alias	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression
Apparentées	5	6
Résumé	Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals.	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.