ロバスト・分位点
18 の手法がこの系統にあります。
注目
不均一分散（HC）頑健標準誤差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. IntrodHuber回帰Huber 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) 回帰分析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推定量（ロバスト回帰）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推定によるロバスト回帰The 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 MQuantile Regression (Nonparametric Variants)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
学びの道筋
このトピックで最も多く参照される基礎的な手法を、発展してきた順に並べました — はじめての方はここから読み始めてください。