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| W-估计量稳健回归（Welsch / Tukey Bisquare）× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1974 | 2019 |
| 提出者≠ | Beaton & Tukey (bisquare weight); Welsch (Welsch weight) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Robust regression (redescending M-estimator) | Linear regression |
| 开创性文献≠ | Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 4 | 5 |
| 摘要≠ | The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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