Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Elastic Net× | Regressão por Mínimos Quadrados Ordinários (MQO)× | |
|---|---|---|
| Área≠ | Estatística | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2005 | 2019 |
| Autor original≠ | Hui Zou and Trevor Hastie | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Penalized linear regression | Linear regression |
| Fonte 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. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Outros nomes | elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateConjunto de dados ↗ |
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