Comparar métodos

	Modelo Robusto com Inflação de Zeros	Regressão Robusta
Área	Estatística	Estatística
Família	Regression model	Regression model
Ano de origem	1990s–2000s	1964
Autor original	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)
Tipo	Robust count regression with excess zeros	Regression with outlier resistance
Fonte seminal	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.
Outros nomes	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Relacionados	5	6
Resumo	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.