Robuste & Quantilsmethoden
18 Methoden in dieser Familie.
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Heteroskedastizitätsrobuste (HC) StandardfehlerHeteroscedasticity-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 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-Schätzer (Robuste 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-Schätzung für robuste 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 MQuantilregression (nichtparametrische Varianten)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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Die meistzitierten grundlegenden Methoden dieses Themas, in der Reihenfolge ihrer Entwicklung — ein Ausgangspunkt, wenn Sie hier neu sind.
Alle Methoden 18
Heteroskedastizitätsrobuste (HC) StandardfehlerHuber-RegressionLeast Trimmed Squares (LTS)-RegressionM-Schätzer (Robuste Regression)MM-Schätzung für robuste RegressionQuantilregression (nichtparametrische Varianten)RANSAC-RegressionRobuste Erklärende ForschungRobustes Gradient BoostingRobust LightGBMRobuste Lineare RegressionRobuste QuantilregressionRobuste RegressionRobuste Regressions-Diskontinuitäts-Analyse (Robust RDD)Robuster XGBoostS-Schätzer für robuste RegressionTheil-Sen-SchätzerW-Schätzer Robuste Regression (Welsch / Tukey Bisquare)