Robust & quantile
18 methods in this family.
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Heteroscedasticity-Robust Standard ErrorsHeteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. IntrodHuber RegressionHuber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differentlLeast Trimmed SquaresLeast Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising tM-EstimatorM-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, tMM-EstimatorThe MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an MNonparametric Quantile RegressionQuantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome r
All methods 18
Heteroscedasticity-Robust Standard ErrorsHuber RegressionLeast Trimmed SquaresM-EstimatorMM-EstimatorNonparametric Quantile RegressionRANSAC RegressionRobust Explanatory ResearchRobust Gradient BoostingRobust LightGBMRobust Linear RegressionRobust Quantile RegressionRobust RegressionRobust Regression Discontinuity DesignRobust XGBoostS-EstimatorTheil-Sen EstimatorW-Estimator