Vertaile menetelmiä
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| Robust Regression× | Lasso-regressio× | OLS-regressio (Ordinary Least Squares)× | |
|---|---|---|---|
| Tieteenala≠ | Tilastotiede | Koneoppiminen | Ekonometria |
| Menetelmäperhe≠ | Regression model | Machine learning | Regression model |
| Syntyvuosi≠ | 1964 | 1996 | 2019 |
| Kehittäjä≠ | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) | Tibshirani, R. | Wooldridge (textbook treatment); classical least squares |
| Tyyppi≠ | Regression with outlier resistance | Regularized linear regression (L1 penalty) | Linear regression |
| Alkuperäislähde≠ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Rinnakkaisnimet | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Liittyvät≠ | 6 | 4 | 5 |
| Tiivistelmä≠ | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. | 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. | 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). |
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