Comparer des méthodes

	Bayes empirique	Modèle à effets mixtes
Domaine	Bayésien	Statistique
Famille	Bayesian methods	Regression model
Année d'origine	—	1982
Auteur d'origine	Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)	Laird & Ware
Type	Empirical Bayes estimator	Mixed effects regression
Source fondatrice	Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press.	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
Alias	EB, empirical Bayes estimation, marginal likelihood estimation, James-Stein shrinkage	LME, LMM, mixed model, random effects model
Apparentées	4	4
Résumé	Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian inference.	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.