Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression de Poisson robuste× | Régression Robuste× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004 | 1964 |
| Auteur d'origine≠ | Guangyong Zou | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Type≠ | GLM with robust variance | Regression with outlier resistance |
| Source fondatrice≠ | Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Alias | modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Apparentées≠ | 5 | 6 |
| Résumé≠ | Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression. | 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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