Robuste og kvantilmetoder
18 metoder i denne familie.
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Heteroscedasticitets-robuste (HC) standardfejlHeteroscedasticity-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 differentlMindste Trimmede Kvadraters (LTS) RegressionLeast 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 Regression)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 regressionThe 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 MKvantilregression (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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Dette emnes mest refererede grundlæggende metoder, i den rækkefølge de blev udviklet — et godt sted at begynde, hvis du er ny her.
Alle metoder 18
Heteroscedasticitets-robuste (HC) standardfejlHuber-regressionMindste Trimmede Kvadraters (LTS) RegressionM-estimatorer (Robust Regression)MM-estimering for robust regressionKvantilregression (ikke-parametriske varianter)RANSAC-regressionRobust Forklarende ForskningRobust Gradient BoostingRobust LightGBMRobust lineær regressionRobust KvantilregressionRobust RegressionRobust Regression Discontinuity-designRobust XGBoostS-estimator til robust regressionTheil-Sen EstimatorW-Estimator Robust Regression (Welsch / Tukey Bisquare)