方法对比
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| MM估计量稳健回归× | 分位数回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1987 | 1978 |
| 提出者≠ | Victor J. Yohai | Koenker & Bassett |
| 类型≠ | Robust linear regression | Conditional quantile regression |
| 开创性文献≠ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. | 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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