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| Model Lineal General Robusta× | Regressió logística robusta× | |
|---|---|---|
| Camp | Estadística | Estadística |
| Família | Regression model | Regression model |
| Any d'origen | 2001 | 2001 |
| Autor original≠ | Cantoni & Ronchetti | Cantoni & Ronchetti (2001); Bondell (2008) |
| Tipus≠ | Robust regression model | Robust generalized linear model (binary outcome) |
| Font seminal≠ | Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264 | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ |
| Àlies | robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon |
| Relacionats | 5 | 5 |
| Resum≠ | 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 Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008). |
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