方法对比
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| M估计量（稳健回归）× | 最小裁剪平方和（LTS）回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2009 | 1984 |
| 提出者≠ | Peter J. Huber | Peter J. Rousseeuw |
| 类型 | Robust linear regression | Robust linear regression |
| 开创性文献≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| 别名≠ | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| 相关 | 5 | 5 |
| 摘要≠ | M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit. | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. |
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