手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| M推定量（ロバスト回帰）× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2009 | 2019 |
| 提唱者≠ | Peter J. Huber | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Robust linear regression | Linear regression |
| 原典≠ | Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名 | m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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. | 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). |
| ScholarGateデータセット ↗ |
|
|