Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustā vienkāršā lineārā regresija× | Robustā daudzkārtējā lineārā regresija× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1964-1987 | 1964–1980s |
| Autors≠ | Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987) | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Tips | Robust linear regression | Robust linear regression |
| Pirmavots≠ | Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339 | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Citi nosaukumi | robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret. | Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients. |
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