Regression model
Robust Logistic Regression
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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Sources
- Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI: 10.1198/016214501753209004 ↗
- Bondell, H. D. (2008). Robust Logistic Regression Using a Weighting Approach. Biometrics, 64(2), 421-427. DOI: 10.1111/j.1541-0420.2007.00875.x ↗