方法对比
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| 稳健线性混合效应模型× | 稳健回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2016 | 1964 |
| 提出者≠ | Richardson & Welsh (robust REML); Koller (robustlmm implementation) | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| 类型≠ | Robust linear mixed-effects model | Regression with outlier resistance |
| 开创性文献≠ | Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| 别名 | robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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). | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
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