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| Methode der kleinsten Quadrate (OLS)× | Quantile Regression× | Ridge Regression× | |
|---|---|---|---|
| Fachgebiet≠ | Ökonometrie | Ökonometrie | Maschinelles Lernen |
| Familie≠ | Regression model | Regression model | Machine learning |
| Entstehungsjahr≠ | 2019 | 1978 | 1970 |
| Urheber≠ | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Linear regression | Conditional quantile regression | L2-regularized linear regression |
| Wegweisende Quelle≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Aliasnamen≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwandt≠ | 5 | 5 | 4 |
| Zusammenfassung≠ | 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). | 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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