Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| LASSO regresija× | Fiksēto efektu paneļa datu modelis× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Ekonometrija |
| Saime≠ | Machine learning | Regression model |
| Izcelsmes gads≠ | 1996 | 2014 |
| Autors≠ | Tibshirani, R. | Hsiao (textbook treatment); within transformation of panel data |
| Tips≠ | Regularized linear regression (L1 penalty) | Panel data regression |
| Pirmavots≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Citi nosaukumi | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
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