Robusna i kvantilna regresija
18 metoda u ovoj obitelji.
Izdvojeno
Robustni (HC) standardni pogrešci kod heteroskedastičnostiHeteroscedasticity-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. IntrodHuberova regresijaHuber 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 differentlRegresija najmanjih podrezanih kvadrata (LTS)Least 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-procjenitelji (Robustna regresija)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-procjena za robusnu regresijuThe 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 MKvantilna regresija (neparametarske varijante)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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Sve metode 18
Robustni (HC) standardni pogrešci kod heteroskedastičnostiHuberova regresijaRegresija najmanjih podrezanih kvadrata (LTS)M-procjenitelji (Robustna regresija)MM-procjena za robusnu regresijuKvantilna regresija (neparametarske varijante)RANSAC regresijaRobustna eksplanatorna istraživanjaRobusno pojačanje gradijentaRobust LightGBMRobustna linearna regresijaRobusna kvantilna regresijaRobustna regresijaRobusni dizajn diskontinuiteta regresijeRobusni XGBoostS-procjenitelj za robusnu regresijuTheil-Senov procjeniteljRobusna regresija W-procjeniteljem (Welsch / Tukey Bisquare)