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| Estimateurs robustes de l'échelle Sn et Qn× | Modèle linéaire mixte robuste× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1993 | 2016 |
| Auteur d'origine≠ | Rousseeuw & Croux | Richardson & Welsh (robust REML); Koller (robustlmm implementation) |
| Type≠ | Robust scale estimator | Robust linear mixed-effects model |
| Source fondatrice≠ | Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283. DOI ↗ | Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. DOI ↗ |
| Alias≠ | Sn estimator, Qn estimator, Rousseeuw-Croux scale estimators, robust scale estimation | robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | Sn and Qn are robust estimators of scale (spread) proposed by Rousseeuw and Croux (1993) as alternatives to the median absolute deviation (MAD). Both attain a 50% breakdown point while delivering higher statistical efficiency than MAD, so they measure dispersion accurately even when the data contain outliers. | The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016). |
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