Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle Linéaire Hiérarchique Robuste× | Régression linéaire multiple robuste× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004 | 1964–1980s |
| Auteur d'origine≠ | Maas & Hox (2004); Goldstein et al. (2018) | Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna |
| Type≠ | Robust multilevel regression | Robust 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 ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Alias | robust HLM, robust multilevel model, robust mixed-effects linear model, robust nested regression | robust MLR, M-estimator regression, resistant multiple regression, robust OLS |
| Apparentées≠ | 5 | 6 |
| 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. | Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients. |
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