Robust dan kuantil
18 metode dalam keluarga ini.
Unggulan
Galat Baku (Standard Errors) Robust terhadap Heteroskedastisitas (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. IntrodRegresi HuberHuber 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 differentlRegresi Least Trimmed Squares (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-Estimator (Regresi Robust)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, tEstimasi MM untuk Regresi RobustThe 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 MNonparametric Quantile RegressionQuantile 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
Jalur bacaan
Metode fondasional yang paling banyak dirujuk pada topik ini, dalam urutan pengembangannya — tempat untuk memulai jika Anda baru di sini.
Semua metode 18
Galat Baku (Standard Errors) Robust terhadap Heteroskedastisitas (HC)Regresi HuberRegresi Least Trimmed Squares (LTS)M-Estimator (Regresi Robust)Estimasi MM untuk Regresi RobustNonparametric Quantile RegressionRegresi RANSACPenelitian Eksplanatori yang RobustGradient Boosting RobustRobust LightGBMRegresi Linier RobustRegresi Kuantil RobustRegresi RobustDesain Regresi Diskontinuitas yang RobustXGBoost RobustEstimator-S untuk Regresi RobustEstimator Theil-SenRegresi Robust W-Estimator (Welsch / Tukey Bisquare)