Comparer des méthodes

	Modèle robuste à zéros inflationnistes	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	1964
Auteur d'origine	Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Robust count regression with excess zeros	Regression with outlier resistance
Source fondatrice	Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	5	6
Résumé	The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations.	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.