Methoden vergleichen

	Mixed Effects Model	Ridge Regression
Fachgebiet	Statistik	Maschinelles Lernen
Familie	Regression model	Machine learning
Entstehungsjahr	1982	1970
Urheber	Laird & Ware	Hoerl, A.E. & Kennard, R.W.
Typ	Mixed effects regression	L2-regularized linear regression
Wegweisende Quelle	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Aliasnamen	LME, LMM, mixed model, random effects model	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Verwandt	4	4
Zusammenfassung	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.