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| Odporny uogólniony model liniowy× | Wytrzymała (robustna) regresja liniowa wielorazowa× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2001 | 1964–1980s |
| Twórca≠ | Cantoni & Ronchetti | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Typ≠ | Robust regression model | Robust linear regression |
| Źródło pierwotne≠ | Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264 | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Inne nazwy | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution. | 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. |
| ScholarGateZbiór danych ↗ |
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