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| Modèle Linéaire Hiérarchique Robuste× | Modèle Linéaire Hiérarchique (HLM)× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004 | 1992 |
| Auteur d'origine≠ | Maas & Hox (2004); Goldstein et al. (2018) | Bryk & Raudenbush |
| Type≠ | Robust multilevel regression | Multilevel linear regression |
| Source fondatrice≠ | Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137. DOI ↗ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 |
| Alias | robust HLM, robust multilevel model, robust mixed-effects linear model, robust nested regression | HLM, multilevel linear model, nested data model, random coefficient model |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Robust Hierarchical Linear Model (Robust HLM) extends standard HLM by replacing or protecting its standard errors against violations of distributional assumptions — chiefly non-normal residuals, heteroscedasticity, and influential clusters. It retains the nested, two-level (or higher) structure while producing more trustworthy inference under real-world data conditions. | 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. |
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