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| Modello Lineare Generalizzato Robusto× | Regressione di Poisson Robusta× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2001 | 2004 |
| Ideatore≠ | Cantoni & Ronchetti | Guangyong Zou |
| Tipo≠ | Robust regression model | GLM with robust variance |
| Fonte seminale≠ | Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264 | Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706. DOI ↗ |
| Alias | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson |
| Correlati | 5 | 5 |
| Sintesi≠ | 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 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. |
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