방법 비교

	강건 선형 혼합 효과 모형	Robust Regression
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	2016	1964
창시자	Richardson & Welsh (robust REML); Koller (robustlmm implementation)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
유형	Robust linear mixed-effects model	Regression with outlier resistance
원전	Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
별칭	robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
관련	5	6
요약	The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016).	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.