Robuste og kvantilmetoder
18 metoder i denne familien.
Utvalgte
Standardfeil for heteroskedastisitet-robust (HC)Heteroscedasticity-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-regresjonHuber 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 Squares (LTS) regresjonLeast 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-estimatorer (Robust regresjon)M-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-estimering for robust regresjonThe 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 MKvantilregresjon (ikke-parametriske varianter)Quantile 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
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Alle metoder 18
Standardfeil for heteroskedastisitet-robust (HC)Huber-regresjonLeast Trimmed Squares (LTS) regresjonM-estimatorer (Robust regresjon)MM-estimering for robust regresjonKvantilregresjon (ikke-parametriske varianter)RANSAC-regresjonRobust forklarende forskningRobust Gradient BoostingRobust LightGBMRobust lineær regresjonRobust KvantilregresjonRobust regresjonRobust Regression Discontinuity DesignRobust XGBoostS-estimator for robust regresjonTheil-Sen-estimatorW-estimator Robust Regression (Welsch / Tukey Bisquare)