Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Robuust Gegeneraliseerd Lineair Model× | Robuuste meervoudige lineaire regressie× | |
|---|---|---|
| Vakgebied | Statistiek | Statistiek |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2001 | 1964–1980s |
| Grondlegger≠ | Cantoni & Ronchetti | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Type≠ | Robust regression model | Robust linear regression |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Verwant≠ | 5 | 6 |
| Samenvatting≠ | 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. |
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