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| 稳健线性回归× | 分位数回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 计量经济学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 1964–1987 | 1978 |
| 提出者≠ | Huber, P. J.; Rousseeuw, P. J. | Koenker & Bassett |
| 类型≠ | Outlier-resistant supervised regression | Conditional quantile regression |
| 开创性文献≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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