Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle à effets mixtes× | Modèle Linéaire Généralisé (GLM)× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1982 | 1972 |
| Auteur d'origine≠ | Laird & Ware | John A. Nelder & Robert W. M. Wedderburn |
| Type≠ | Mixed effects regression | Regression framework |
| Source fondatrice≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ |
| Alias | LME, LMM, mixed model, random effects model | GLM, generalized regression, exponential family regression, link-function model |
| Apparentées≠ | 4 | 6 |
| Résumé≠ | 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. | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. |
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