Regression modelRegression / GLM
Robust Regression
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.
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Sources
- Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI: 10.1214/aoms/1177703732 ↗
- Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley. ISBN: 978-0471735779
Related methods
Referenced by
Bayesian Robust RegressionElastic Net RegressionInfluence DiagnosticsOrdinary Least SquaresRobust ARCH modelRobust Cox RegressionRobust Generalized linear modelRobust Hierarchical Linear ModelRobust Mixed ModelRobust Multinomial Logistic RegressionRobust Multiple linear regressionRobust Negative Binomial RegressionRobust PCARobust Poisson RegressionRobust Probit ModelRobust Quantile RegressionRobust Quantile-on-Quantile RegressionRobust Ridge regressionRobust SARIMA modelRobust Simple linear regressionRobust Zero-Inflated ModelWeighted Least Squares