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| Bayesiansk LASSO-regression× | Elastic Net-regression× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2008 | 2005 |
| Upphovsperson≠ | Park & Casella | Hui Zou and Trevor Hastie |
| Typ≠ | Bayesian regularized regression | Penalized linear regression |
| Ursprungskälla≠ | Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686. DOI ↗ | 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 ↗ |
| 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 |
| Närliggande≠ | 5 | 6 |
| Sammanfattning≠ | 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. |
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