Robusta metoder & kvantilregression
18 metoder i denna familj.
I urval
Standardfel för heteroskedasticitet (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. IntrodHuberregressionHuber 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) 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 för 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 (icke-parametriska 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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Det här ämnets mest refererade grundläggande metoder, i den ordning de utvecklades — en bra startpunkt om du är ny här.
Alla metoder 18
Standardfel för heteroskedasticitet (HC)HuberregressionLeast Trimmed Squares (LTS) RegressionM-estimatorer (Robust Regression)MM-estimering för robust regressionKvantilregression (icke-parametriska varianter)RANSAC-regressionRobust förklarande forskningRobust Gradient BoostingRobust LightGBMRobust linjär regressionRobust KvantilregressionRobust regressionRobust Regression Discontinuity DesignRobust XGBoostS-skattare för robust regressionTheil-Sen EstimatorW-estimator robust regression (Welsch / Tukey bisquare)