Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Robustne üldistatud lineaarne mudel× | Robust Regression× | |
|---|---|---|
| Valdkond | Statistika | Statistika |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2001 | 1964 |
| Looja≠ | Cantoni & Ronchetti | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Tüüp≠ | Robust regression model | Regression with outlier resistance |
| Algallikas≠ | 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. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Rööpnimetused | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Seotud≠ | 5 | 6 |
| Kokkuvõte≠ | 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 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. |
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