方法对比
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| 最小中位数平方（LMS）回归× | 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≠ | 1984 | 2019 |
| 提出者≠ | Peter J. Rousseeuw | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Robust linear regression | Linear regression |
| 开创性文献≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | LMS, least median of squares regression, en küçük medyan kareler (LMS) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers. | 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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