Comparer des méthodes

	Modèle Linéaire Hiérarchique (HLM)	Modèle Linéaire Généralisé (GLM)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1992	1972
Auteur d'origine	Bryk & Raudenbush	John A. Nelder & Robert W. M. Wedderburn
Type	Multilevel linear regression	Regression framework
Source fondatrice	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Alias	HLM, multilevel linear model, nested data model, random coefficient model	GLM, generalized regression, exponential family regression, link-function model
Apparentées	4	6
Résumé	The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.	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.