Porównaj metody

	Solidny liniowy model mieszany	Błędy standardowe odporne na heteroskedastyczność (HC)
Dziedzina	Statystyka	Statystyka
Rodzina	Regression model	Regression model
Rok powstania	2016	1980
Twórca	Richardson & Welsh (robust REML); Koller (robustlmm implementation)	Eicker; Huber; White (1980); MacKinnon & White (1985)
Typ	Robust linear mixed-effects model	Robust covariance estimator for linear regression
Źródło pierwotne	Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24.	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.
Inne nazwy	robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors
Pokrewne	5	5
Podsumowanie	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).	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. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.